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Towards explicit representation of an artificial neural network model: Comparison of two artificial neural network rule extraction approaches

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Petroleum 2020年第4期6卷 329-339页

作者： Veronica K.H.Chan Christine W.Chan Energy Informatics Laboratory Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada

In the quest for interpretable models,two versions of a neural network rule extraction algorithm were proposed and compared.The two algorithms are called the Piece-Wise Linear Artificial Neural Network(PWL-ANN)and enhanced Piece-Wise Linear Artificial Neural Network(enhanced PWL-ANN)algorithms.The PWL-ANN algorithm is a decomposition artificial neural network(ANN)rule extraction algorithm,and the enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm and extracts multiple linear regression equations from a trained ANN model by approximating the hidden sigmoid activation functions using N-piece linear equations.In doing so,the algorithm provides interpretable models from the originally trained opaque ANN models.A detailed application case study illustrates how the generated enhanced-PWL-ANN models can provide understandable IF-THEN rules about a problem domain.Comparison of the results generated by the two versions of the PWL-ANN algorithm showed that in comparison to the PWL-ANN models,the enhanced-PWL-ANN models support improved fidelities to the originally trained ANN models.The results also showed that more concise rule sets could be generated using the enhanced-PWL-ANN algorithm.If a more simplified set of rules is desired,the enhanced-PWL-ANN algorithm can be combined with the decision tree approach.Potential application of the algorithms to domains related to petroleum engineering can help enhance understanding of the problems.

关键词： Artificial neural networks rule extraction Regression problem Algorithm design

A RAPID FUZZY rule extraction METHOD FOR FUZZY CONTROLLER

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2000年第3期 74-79页

作者：杨建刚王如明

Based on division of the three dimensional space from data samples, the method proposed in this paper can rapidly extract fuzzy rules by using the fuzzy information of the samples. The principle of this approach is proved theoretically. Due to its simplicity this method can be used to extract fuzzy rules in real time for an adaptive control system. Simulation results showed that this approach is effective and practical.

关键词： fuzzy control rule extraction space division sample reappearance

Frequency-constrained Unit Commitments with Linear rules Extracted from Simulation Results Considering Regulations from Battery Storage

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1041-1052页

作者： Jiawei Zhang Zhichao Chen Ning Zhang Xianfeng Zhang State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Science and Technology Research Institute ChinaThreeGorgesCorporationBeijing100038China

Heavy renewable penetrations and high-voltage cross-regional transmission systems reduce the inertia and critical frequency stability of power systems after disturbances.Therefore,the power system operators should ensure the frequency nadirs after possible disturbances are within the set restriction,e.g.,0.20 Hz.Traditional methods utilize linearized and simplified control models to quantify the frequency nadirs and achieve frequency-constrained unit commitments(FCUCs).However,the simplified models are hard to depict the frequency responses of practical units after disturbances.Also,they usually neglect the regulations from battery storage.This paper achieves FCUCs with linear rules extracted from massive simulation results.We simulate the frequency responses on typical thermal-hydro-storage systems under diverse unit online conditions.Then,we extract the rules of frequency nadirs after disturbances merely with linear support vector machine to evaluate the frequency stability of power systems.The algorithm holds a high accuracy in a wide range of frequency restrictions.Finally,we apply the rules to three typical cases to show the influences of frequency constraints on unit commitments.

关键词： Frequency regulation unit commitment machine learning rule extraction primary frequency regulation energy storage support vector machine

Stock Price Forecasting and rule extraction Based on L1-Orthogonal Regularized GRU Decision Tree Interpretation Model

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国际计算机前沿大会会议论文集 2020年第2期 309-328页

作者： Wenjun Wu Yuechen Zhao Yue Wang Xiuli Wang School of Information Central University of Finance and EconomicsBeijingChina

Neural network is widely used in stock price forecasting,but it lacks interpretability because of its“black box”characteristics.In this paper,L1-orthogonal regularization method is used in the GRU model.A decision tree,GRU-DT,was conducted to represent the prediction process of a neural network,and some rule screening algorithms were proposed to find out significant rules in the prediction.In the empirical study,the data of 10 different industries in China’s CSI 300 were selected for stock price trend prediction,and extracted rules were compared and analyzed.And the method of technical index discretization was used to make rules easy for decision-making.Empirical results show that the AUC of the model is stable between 0.72 and 0.74,and the value of F1 and Accuracy are stable between 0.68 and 0.70,indicating that discretized technical indicators can predict the short-term trend of stock price effectively.And the fidelity of GRU-DT to the GRU model reaches 0.99.The prediction rules of different industries have some commonness and individuality.

关键词： Explainable artificial intelligence Neural network interpretability rule extraction Stock forecasting L1-orthogonal regularization

extraction Fuzzy Linguistic rules from Neural Networks for Maximizing Tool Life in High-speed Milling Process

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Chinese Journal of Mechanical Engineering 2009年第3期22卷 341-346页

作者： SHEN Zhigang HE Ning LI Liang College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate（MRR）. After the advent of high-speed milling（HSM） pro cess, lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network（FNN） so as to have the most effective knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed. After training process, raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules. The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.

关键词： high-speed milling rule extraction neural network fuzzy logic

EasySVM: A visual analysis approach for open-box support vector machines

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Computational Visual Media 2017年第2期3卷 161-175页

作者： Yuxin Ma Wei Chen Xiaohong Ma Jiayi Xu Xinxin Huang Ross Maciejewski Anthony K.H.Tung State Key Lab of CAD&CG Zhejiang University Arizona State University National University of Singapore

Support vector machines(SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly.The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner.Our goal is to improve an analyst's understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process.Our visual exploration tools have been developed to enable intuitive parameter tuning, training datamanipulation, and rule extraction as part of the SVM training process. To demonstrate the efficacy of our approach, we conduct a case study using a real-world robot control dataset.

关键词： support vector machines(SVMs) rule extraction visual classification high-dimensional visualization visual analysis

Data Mining Based on Computational Intelligence

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Wuhan University Journal of Natural Sciences 2005年第2期10卷 371-374页

作者： WANGYuan-zhen ZHANGZhi-bing YIBao-lin LIHua-yang CollegeofComputerScienceandTechnology HuazhongUniversityofScienceandTechnologyWuhan430074HubeiChina SchoolofComputerScienceandTechnology HuazhongNormalUniversityWuhan130079HubeiChina

This paper combines computational intelligence tools: neural network, fuzzylogic, and genetic algorithm to develop a data mining architecture (NFGDM), which discovers patternsand represents them in understandable forms. In the NFGDM, input data arepreprocesscd byfuzzification, the preprocessed data of input variables arc then used to train a radial basisprobabilistic neural network to classify the dataset according to the classes considered, A ruleextraction technique is then applied in order to extract explicit knowledge from the trained neuralnetworks and represent it m the form of fuzzy if-then rules. In the final stage, genetic algorithmis used as a rule-pruning module to eliminate those weak rules that are still in the rule bases.Comparison with some known neural network classifier, the architecture has fast learning speed, andit is characterized by the incorporation of the possibility information into the consequents ofclassification rules in human understandable forms. The experiments show that the NFGDM is moreefficient and more robust than traditional decision tree method.

关键词： data mining rule extraction neural network fuzzy logic genetic algorithm

A Recursive Method of Learning Bayesian Network for rule extraction Based on Information Theory

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A Recursive Method of Learning Bayesian Network for Rule Ext...

Expert System Based on Data Mining and Neural Networks

第一届建立和谐人机环境联合学术会议（HHME2005）

作者： Yangu Zhang1,Ming Yao2, Bin Shen2 1 (School of Computer Science & Engineering, Wenzhou University, Wenzhou, Zhejiang 325027) 2 (School of Computer,Zhejiang University,Hangzhou,Zhejiang 310028)

This paper presents a kind of recursive method for learning Bayesian networks (BNs) used to mine correlation rules. Starting from the target attributes (results), candidate attributes that impose on t

关键词： rule extraction Bayesian network information theory recursive method.

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Journal of Systems Science and Systems Engineering 2001年第3期13卷 323-327页

作者： NI Zhi-wei 1,2 ,\ JIA Rui-yu 1 1.Department of Computer Science, Anhui University, Hefei 230039, China 2.Department of Computer Science, University of Science and Technology of China, Hefei 230027, China Department of Computer Science Anhui University He fei 230039 China Department of Computer Science University of Science and Technology of China Hefei 230027 China Department of Computer Science Anhui University He fei 230039 China

On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural... 详细信息

关键词： expert system neural networks data mining rule extraction rule evaluation