Research

21 pages, 2544 KiB

Open AccessArticle

A Deep Learning Neural Network Method Using Linear Eigenvalue Statistics for Schizophrenic EEG Data Classification

by Haichun Liu, Lanzhen Li, Yumeng Ye, Changchun Pan, Genke Yang, Tao Chen, Tianhong Zhang, Jijun Wang and Caiming (Robert) Qiu

Mathematics 2023, 11(23), 4776; https://doi.org/10.3390/math11234776 - 26 Nov 2023

Cited by 1 | Viewed by 1264

Abstract

Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies [...] Read more.

Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies in the inadequacy of both objective characteristics and effective data analysis techniques. Random matrix theory (RMT) and its linear eigenvalue statistics (LES) can provide an effective mathematical modeling method for exploring the statistical properties of non-stationary nonlinear systems, such as EEG signals. To obtain an accurate classification and diagnosis of schizophrenia, this paper proposes a LES-based deep learning network scheme in which a series of random matrixes, consisting of EEG data sliding window sampling and their eigenvalues, are employed as features for deep learning. Due to the fact that the performance of the LES-based scheme is sensitive to the LES’s test function, the proposed LES-based deep learning network is embedded with two ways of combining LES’s test functions with learning techniques: the first is to have the LES’s test function assigned, while, using the second way, the optimal LES’s test function should be solved in a functional optimization problem. In this paper, various test functions and different optimal learning methods were coupled in experiments. Our results revealed a binary classification accuracy of nearly 90% in distinguishing between healthy controls (HC) and patients experiencing the first episode of schizophrenia (FES). Additionally, we achieved a ternary classification accuracy of approximately 70% by including clinical high risk for psychosis (CHR). The LES-embedded approach yielded notably higher classification accuracy compared to conventional machine learning methods and standard convolutional neural networks. As the performance of schizophrenia classification is strongly influenced by test functions, a functional optimization problem was proposed to identify an optimized test function, and an approximated parameter optimization problem was introduced to limit the search area of suitable basis functions. Furthermore, the parameterization test function optimization problem and the deep learning network were coupled to be synchronously optimized during the training process. The proposal approach achieved higher classification accuracy rates of 96.87% between HC and FES, with an additional 89.06% accuracy when CHR was included. The experimental studies demonstrated that the proposed LES-based method was significantly effective for schizophrenic EEG data classification. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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12 pages, 297 KiB

Open AccessArticle

Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty

by Xuecheng Tian, Yanxia Guan and Shuaian Wang

Mathematics 2023, 11(17), 3782; https://doi.org/10.3390/math11173782 - 03 Sep 2023

Viewed by 971

Abstract

Decision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today’s big data age, while the predict-then-optimize framework has become a standard method [...] Read more.

Decision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today’s big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-making quality. To enhance predictive accuracy and consequent decision-making quality, we introduce a data transformation technique into the predict-then-optimize framework. Our approach transforms target values in linear regression, decision tree, and random forest models using a power function, aiming to boost their predictive prowess and, in turn, drive better decisions. Empirical validation on several datasets reveals marked improvements in decision tree and random forest models. In contrast, the benefits of linear regression are nuanced. Thus, while data transformation can bolster the predict-then-optimize framework, its efficacy is model-dependent. This research underscores the potential of tailoring transformation techniques for specific models to foster reliable and robust decision-making under uncertainty. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

19 pages, 2837 KiB

Open AccessArticle

Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide-ℓ_p Penalty and Deep Learning Approach

by Wanying Song, Jian Min and Jianbo Yang

Mathematics 2023, 11(16), 3462; https://doi.org/10.3390/math11163462 - 09 Aug 2023

Viewed by 897

Abstract

Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to [...] Read more.

Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to increase, so does the irrelevant feature or noise. Therefore, we investigate the use of non-integers for regularization from high-dimensional data under the conditions of a large number of irrelevant features. In this paper, a novel Wide-ℓ_p Penalty and Deep Learning (WPDL) method for credit risk assessment is proposed, which could provide a sparse solution. The Wide-ℓ_p Penalty component allows feature selection using a linear model with an ℓ_p Penalty regularization mechanism, where 0 < p ≤ 2. The deep component is a DNN that can generalize indicator features from the credit risk data. The experimental results show that the minimum prediction error occurs at a non-integer ℓ_p Penalty. Furthermore, the WPDL outperforms other models such as KNN, DT, RF, SVM, MLP, DNN, Gradient Boosting, and Bagging. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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24 pages, 950 KiB

Open AccessArticle

Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation

by Xiaoyu Yu, Jingyi Qian, Yajing Zhang and Min Kong

Mathematics 2023, 11(15), 3276; https://doi.org/10.3390/math11153276 - 26 Jul 2023

Cited by 1 | Viewed by 861

Abstract

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port [...] Read more.

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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11 pages, 902 KiB

Open AccessArticle

Efficiency Evaluation of China’s Provincial Digital Economy Based on a DEA Cross-Efficiency Model

by Yaqiao Xu, Jiayi Hu and Liusan Wu

Mathematics 2023, 11(13), 3005; https://doi.org/10.3390/math11133005 - 06 Jul 2023

Cited by 2 | Viewed by 821

Abstract

The Chinese government clearly put forward a strategy to speed up the development of the digital economy in “the 14th Five-Year” Plan, which will become the booster of China’s development. China has a vast territory and the state of development of the digital [...] Read more.

The Chinese government clearly put forward a strategy to speed up the development of the digital economy in “the 14th Five-Year” Plan, which will become the booster of China’s development. China has a vast territory and the state of development of the digital economy varies greatly across different regions. It is crucial to clarify the reasons for these differences and take measures to narrow them. Therefore, the evaluation and analysis of the current situation are conducive to the further development of the digital economy. Taking 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) of China as the research objects, this paper constructs an index system taking digital infrastructure, digital technology and digital talent as input variables and taking digital industrialization and industrial digitization as output variables. The data envelopment analysis (DEA) cross-efficiency model is constructed to calculate and compare the cross-efficiency of the digital economies in each province. The results show the following: (1) The development efficiency of China’s digital economy has generally been low, and there is a large “digital divide” between provinces. (2) The input of digital talents is crucial for the digital economy in order to achieve high output and high efficiency, and high output is often accompanied by high efficiency. Based on the above conclusions, this paper puts forward some suggestions to promote the development of China’s digital economy. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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16 pages, 5395 KiB

Open AccessArticle

Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network

by Ming Jiang and Zhiwei Liu

Mathematics 2023, 11(11), 2528; https://doi.org/10.3390/math11112528 - 31 May 2023

Cited by 3 | Viewed by 1926

Abstract

More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies [...] Read more.

More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks to model traffic graphs and attempt to use fixed graph structures to obtain relationships between nodes. However, due to the time-varying spatial correlation of the transportation network, there is no stable node relationship. To address the above issues, we propose a new traffic prediction framework called the Dynamic Graph Spatial-Temporal Neural Network (DGSTN). Unlike other models that use predefined graphs, this model represents stable node relationships and time-varying node relationships by constructing static topology maps and dynamic information maps during the training and testing stages, to capture hidden node relationships and time-varying spatial correlations. In terms of network architecture, we designed multi-scale causal convolution and adaptive spatial self-attention mechanisms to capture temporal and spatial features, respectively, and assisted learning through static and dynamic graphs. The proposed framework has been tested on two real-world traffic datasets and can achieve state-of-the-art performance. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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25 pages, 5841 KiB

Open AccessArticle

A Text-Oriented Fault Diagnosis Method for Electromechanical Device Based on Belief Rule Base

by Manlin Chen, Zhijie Zhou, Xiaoxia Han and Zhichao Feng

Mathematics 2023, 11(8), 1814; https://doi.org/10.3390/math11081814 - 11 Apr 2023

Viewed by 1052

Abstract

At present, quantitative data is often used for fault diagnosis of electromechanical devices, while qualitative data in the form of text is rarely used. In order to integrate qualitative data in the form of text and quantitative data in the fault diagnosis of [...] Read more.

At present, quantitative data is often used for fault diagnosis of electromechanical devices, while qualitative data in the form of text is rarely used. In order to integrate qualitative data in the form of text and quantitative data in the fault diagnosis of an electromechanical device, a text-oriented fault diagnosis method based on belief rule base (BRB) is proposed in this paper. Specifically, the key information of fault diagnosis is extracted from the text through natural language processing (NLP) and then converted into belief rules. Then, a rule supplement method is adopted to add the extracted belief rules to the BRB for the completion of the BRB construction. This method applies qualitative data in the form of text to the process of BRB construction, which is a new attempt at the BRB construction method. It not only solves the problem that BRB cannot use qualitative data in text form but also improves the modeling accuracy and data comprehensive processing ability of BRB. To verify the effectiveness of the algorithm, we designed an experiment of asynchronous motor fault diagnosis in the case study. The experimental result shows that the proposed method can use qualitative data in text form to construct BRB and effectively diagnose faults of asynchronous motors. The MSE of the proposed method is 0.0451, which is better than that of traditional BRB (0.1461), BP (0.0613), and SVR (0.0974) under the same experimental conditions. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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26 pages, 5237 KiB

Open AccessArticle

Learning Emotion Assessment Method Based on Belief Rule Base and Evidential Reasoning

by Haobing Chen, Guohui Zhou, Xin Zhang, Hailong Zhu and Wei He

Mathematics 2023, 11(5), 1152; https://doi.org/10.3390/math11051152 - 26 Feb 2023

Cited by 1 | Viewed by 1113

Abstract

Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertainty in the [...] Read more.

Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertainty in the process of data collection and model parameter errors. Given the above problems, a new learning emotion assessment model based on evidence reasoning and a belief rule base (E-BRB) is proposed in this paper. First, the transformation matrix is introduced to transform multiple emotional indicators into the same standard framework and integrate them, which keeps the consistency of information transformation. Second, the relationship between emotional indicators and learning emotion states is modeled by E-BRB in conjunction with expert knowledge. In addition, we employ the projection covariance matrix adaptation evolution strategy (P-CMA-ES) to optimize the model parameters and improve the model’s accuracy. Finally, to demonstrate the effectiveness of the proposed model, it is applied to emotion assessment in science learning. The experimental results show that the model has better accuracy than data-driven models such as neural networks. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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23 pages, 3781 KiB

Open AccessArticle

Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule

by Xuelin Zhang, Xiaobin Xu, Xiaojian Xu, Pingzhi Hou, Haibo Gao and Feng Ma

Mathematics 2023, 11(5), 1145; https://doi.org/10.3390/math11051145 - 25 Feb 2023

Cited by 2 | Viewed by 1244

Abstract

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive [...] Read more.

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters K_P, K_I, and K_D. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of K_P, K_I, and K_D. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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21 pages, 912 KiB

Open AccessArticle

Optimal Selection of Stock Portfolios Using Multi-Criteria Decision-Making Methods

by Dongmei Jing, Mohsen Imeni, Seyyed Ahmad Edalatpanah, Alhanouf Alburaikan and Hamiden Abd El-Wahed Khalifa

Mathematics 2023, 11(2), 415; https://doi.org/10.3390/math11020415 - 12 Jan 2023

Cited by 11 | Viewed by 3001

Abstract

In the past, investors used their own or others’ experiences to achieve their goals. With the development of financial management, investors’ choices became more scientific. They could select the optimal choice by using different models and combining the results with their experiences. In [...] Read more.

In the past, investors used their own or others’ experiences to achieve their goals. With the development of financial management, investors’ choices became more scientific. They could select the optimal choice by using different models and combining the results with their experiences. In portfolio optimization, the main issue is the optimal selection of the assets and securities that can be provided with a certain amount of capital. In the present study, the problem of optimization, i.e., maximizing stock portfolio returns and minimizing risk, has been studied. Therefore, this study discussed comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. A sample of 79 companies listed on the Tehran Stock Exchange was used to conduct this research. After simulating the data and programming them with MATLAB software, the cumulative data analysis model was performed, and 24 companies were selected. This research data were collected from the financial statements of companies listed on the Tehran Stock Exchange in 2020. The primary purpose of this study was a comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. The index in the Tehran Stock Exchange can be used to provide a comprehensive and optimal model for the stock portfolio; different multi-index decision-making methods (TOPSIS method), the taxonomy method (Taxonomy), ARAS method, VIKOR method, The COPRAS method and the WASPAS method can all identify the optimal stock portfolio and the best stock portfolio for the highest return. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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18 pages, 3080 KiB

Open AccessArticle

A Data-Driven Process Monitoring Approach Based on Evidence Reasoning Rule Considering Interval-Valued Reliability

by Shanen Yu, Saijun Liu, Xu Weng, Xiaobin Xu, Zhenjie Zhang, Fang Liu, Felix Steyskal and Georg Brunauer

Mathematics 2023, 11(1), 88; https://doi.org/10.3390/math11010088 - 26 Dec 2022

Viewed by 1234

Abstract

In the process industry, an alarm system is one of the important ways of condition monitoring. Due to the complexity and irregularity of process information in condition monitoring, there are too many false alarms in the current alarm system. In order to solve [...] Read more.

In the process industry, an alarm system is one of the important ways of condition monitoring. Due to the complexity and irregularity of process information in condition monitoring, there are too many false alarms in the current alarm system. In order to solve the problem of designing an alarm system, this paper proposes a multivariate alarm design method based on the evidence reasoning (ER) rule, considering interval-valued reliability, which can make full use of process information to make accurate alarm decisions. Firstly, the referential evidence matrixes (REMs) are constructed based on the training samples of process variables, and the real-time samples of the process variables are converted into alarm evidence by activating the REMs. Alarm evidence is then fused by the ER rule. In this fusion process, in order to better describe the uncertainty of the process information, the reliability of the alarm evidence is characterized by random variables with certain probability distributions, and it can be adjusted in dynamic intervals according to the real-time change of alarm evidence. Finally, the reactor fault case is implemented in the Tennessee Eastman (TE) process, which shows that the adjustment of interval-valued reliability can adapt to the irregular change of process information and obtains consistent alarm results to further improve the accuracy of alarm decisions. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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24 pages, 3663 KiB

Open AccessArticle

A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base

by Xiaoyu Cheng, Guangyu Qian, Wei He and Guohui Zhou

Mathematics 2022, 10(24), 4772; https://doi.org/10.3390/math10244772 - 15 Dec 2022

Cited by 3 | Viewed by 1052

Abstract

As the propulsion part of a space launch vehicle and nuclear weapon missile, the health status of the liquid rocket determines whether the space launch vehicle and nuclear weapon missile can function normally. Therefore, it is of great significance to evaluate the health [...] Read more.

As the propulsion part of a space launch vehicle and nuclear weapon missile, the health status of the liquid rocket determines whether the space launch vehicle and nuclear weapon missile can function normally. Therefore, it is of great significance to evaluate the health status of the liquid rocket. As the structure of the liquid rocket is becoming increasingly sophisticated, subjective judgment alone can no longer meet the needs of the actual system. As an expert system and a gray-box model, the belief rule base (BRB) can process both qualitative and quantitative information. The expert knowledge base is used in the safety assessment of a liquid rocket. However, in practical applications, the traditional BRB model still has two problems, which are that (1) when there are too many premise attributes, it easily leads to the explosion of combination rules, and (2) the reliability of rules is not considered in the process of model reasoning. Therefore, this paper proposes the BRB model with intervals (intervals-BRB) on the basis of traditional BRB. The interval-BRB retains the advantage of the traditional BRB, which can handle semi-quantitative information. In addition, the proposed model changes the reference point of the prerequisite attribute to the reference interval and changes the rule combination. This solves the problem of the traditional BRB explosive combination rule. The ER-rule (evidential reasoning rule) is introduced into the reasoning procedure, and the weight of the rule and the reliability of the rule are considered at the same time, which solves the shortcoming of the traditional BRB, which does not consider the reliability of the rule in reasoning. Finally, the CMAES optimization algorithm is used to optimize the initial model to obtain better performance. Finally, the model is verified by the actual data set of a liquid rocket, and the experimental results show that the model can achieve good experimental results. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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19 pages, 373 KiB

Open AccessArticle

Impressionable Rational Choice: Revealed-Preference Theory with Framing Effects

by Guy Barokas and Burak Ünveren

Mathematics 2022, 10(23), 4496; https://doi.org/10.3390/math10234496 - 28 Nov 2022

Viewed by 754

Abstract

Revealed preference is one of the most influential ideas in economics. It is, however, not clear how it can be generally applied in cases where agents’ choices depend on arbitrary changes in the decision environment. In this paper, we propose a generalization of [...] Read more.

Revealed preference is one of the most influential ideas in economics. It is, however, not clear how it can be generally applied in cases where agents’ choices depend on arbitrary changes in the decision environment. In this paper, we propose a generalization of the classic rational choice theory that allows for such framing effects. Frames are modeled as different presentations (e.g., visual or conceptual) of the alternatives that may affect choice. Our main premise is that framing effects are neutral (i.e., independent of labeling the alternatives). An agent exhibiting these neutral framing effects who is otherwise rational, is called impressionable rational. We show that our theory encompasses many familiar behavioral models such as status-quo bias, satisficing, present bias, framing effects resulting from indecisiveness, certain forms of limited attention, categorization bias, and the salience theory of choice, as well as hybrid models. Moreover, in all these models, sufficiently rich choice data allow our theory to identify the “correct” underlying preferences without invoking each specific cognitive process. Additionally, we introduce a falsifiable axiom that completely characterizes the behavior of agents who are impressionable rational. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

16 pages, 3831 KiB

Open AccessArticle

Development of a Robust Data-Driven Soft Sensor for Multivariate Industrial Processes with Non-Gaussian Noise and Outliers

by Yongshi Liu, Xiaodong Yu, Jianjun Zhao, Changchun Pan and Kai Sun

Mathematics 2022, 10(20), 3837; https://doi.org/10.3390/math10203837 - 17 Oct 2022

Cited by 6 | Viewed by 1221

Abstract

Industrial processes are often nonlinear and multivariate and suffer from non-Gaussian noise and outliers in the process data, which cause significant challenges in data-driven modelling. To address these issues, a robust soft-sensing algorithm that integrates Huber’s M-estimation and adaptive regularisations with multilayer perceptron [...] Read more.

Industrial processes are often nonlinear and multivariate and suffer from non-Gaussian noise and outliers in the process data, which cause significant challenges in data-driven modelling. To address these issues, a robust soft-sensing algorithm that integrates Huber’s M-estimation and adaptive regularisations with multilayer perceptron (MLP) is proposed in this paper. The proposed algorithm, called RAdLASSO-MLP, starts with an initially well-trained MLP for nonlinear data-driven modelling. Subsequently, the residuals of the proposed model are robustified with Huber’s M-estimation to improve the resistance to non-Gaussian noise and outliers. Moreover, a double L1-regularisation mechanism is introduced to minimise redundancies in the input and hidden layers of MLP. In addition, the maximal information coefficient (MIC) index is investigated and used to design the adaptive operator for the L1-regularisation of the input neurons to improve biased estimations with L1-regularisation. Including shrinkage parameters and Huber’s M-estimation parameter, the hyperparameters are determined via grid search and cross-validation. To evaluate the proposed algorithm, simulations were conducted with both an artificial dataset and an industrial dataset from a practical gasoline treatment process. The results indicate that the proposed algorithm is superior in terms of predictive accuracy and robustness to the classic MLP and the regularised soft-sensing approaches LASSO-MLP and dLASSO-MLP. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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28 pages, 3738 KiB

Open AccessArticle

A Prospect-Theory-Based Operation Loop Decision-Making Method for Kill Web

by Luyao Wang, Libin Chen, Zhiwei Yang, Minghao Li, Kewei Yang and Mengjun Li

Mathematics 2022, 10(19), 3486; https://doi.org/10.3390/math10193486 - 24 Sep 2022

Cited by 1 | Viewed by 1464

Abstract

In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in [...] Read more.

In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders’ decision-making. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications)

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