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Optimization of type-1, interval type-2 and general type-2 fuzzy inference systems using a hierarchical genetic algorithm for modular granular neural networks

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GRANULAR COMPUTING 2019年第2期4卷 211-236页

作者： Melin, Patricia Sanchez, Daniela Tijuana Inst Technol Tijuana Mexico

In this paper, a new method for fuzzy inference system optimization is presented. The proposed method performs type-1, interval type-2 and general type-2 fuzzy inference systems design using a hierarchical genetic algorithm as optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed using a human recognition application. The human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea is to perform the combination of responses in the modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The main contribution of the proposed method is an optimization able to select type of fuzzy logic, granulation of each fuzzy variable and fuzzy rules selection to design optimal fuzzy inference systems applied in combining modular neural networks responses. The results obtained show the effectiveness of the proposed method to design structures of fuzzy inference systems. Statistical comparisons are performed with previous results, where better results can be observed using the proposed method. The design of optimal structures of fuzzy inference systems include among other parameters;type of fuzzy logic (type-1, interval and general type-2 fuzzy logic), type of inference model (Mamdani model or Sugeno model), and consequents of the fuzzy if-then rules.

关键词： Modular neural networks Type-1 fuzzy logic Interval type-2 fuzzy logic General type-2 fuzzy logic Human recognition hierarchical genetic algorithm

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Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a hierarchical genetic algorithm

Feature Selection of EEG Oscillatory Activity Related to Mot...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Leon, Miguel Ballesteros, Joaquin Tidare, Jonatan Xiong, Ning Astrand, Elaine Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden

ISBN: (纸本)9781728121536

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a hierarchical genetic algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

关键词： Motor Imagery EEG Signal hierarchical genetic algorithm

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An Optimized Power Terminal Communication Access Network Planning Based on hierarchical genetic algorithm 9

An Optimized Power Terminal Communication Access Network Pla...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Fanbo Meng Honghao Zhao Bingyan Yu Qian Liu Qiang Cheng Jie Chen Jian Li State Grid LIAONING Electronic Power Supply Corporation Institute of Technology and Standards Research China Academy of Information and Communications Technology

ISBN: (纸本)9781509038237;9781509038220

Power terminal communication access network(PTCAN) is an important network for smart grid applications due to its ability of monitoring and remotely controlling smart power distribution devices and carrying the consumer information. In this paper, a hierarchical genetic algorithm is proposed to plan and optimize the Industrial Ethernet based PTCAN. The key idea of the algorithm is to transform the treeform topology of power distribution network to a ring-form topology of PTCAN. A depth-first pre-order traversal based chromosome initialization method is raised in this paper and a hierarchical planning scheme is proposed to simplified the algorithm complexity. Corresponding evaluation and selection function, crossover and mutation method are designed and described in detail. At last, a simulation is performed, the result shows our algorithm is fully feasible and has advantages of faster convergence rate and better performance.

关键词： power terminal communication access network Industrial Ethernet hierarchical genetic algorithm depth-first pre-order traversal

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An improved hierarchical genetic algorithm for collaborative optimization of manufacturing processes in metal structure manufacturing systems

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ADVANCES IN MECHANICAL ENGINEERING 2017年第3期9卷

作者： Qi, Dezhong Zhang, Sanqiang Liu, Mingyong Lei, Yakuo Hubei Univ Technol Hubei Agr Machinery Engn Res & Design Inst Wuhan 430068 Peoples R China

The multi-objective optimization problem includes plate nesting, production planning, scheduling, and equipment capacity optimization in the complex manufacturing process of metal structures. For the best optimization results, a global collaborative optimization of the manufacturing system is necessary. A multi-objective optimization model for optimized nesting, optimized scheduling, dispatch optimizing, and equipment load balancing is constructed, and an improved hierarchical genetic algorithm is then developed for a better solution. A hierarchical structure of three chromosomes is designed in this algorithm. The algorithm can be used to simultaneously solve the layout selection, process sequencing, and machine selection problems. The algorithm shortens the production cycle, reduces the number of work in process, and improves equipment utilization through the application of collaborative optimization. The computational result and comparison prove that the presented approach is quite effective to address the considered problem.

关键词： Metal structures collaborative manufacturing hierarchical genetic algorithm cutting collaborative optimization

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Simultaneous optimization of photostrictive actuator locations, numbers and light intensities for structural shape control using hierarchical genetic algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2015年 88卷 21-29页

作者： Zhao, Yu Zheng, Shijie Wang, Hongtao Yang, Liuqing Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

The present paper introduces an investigation into simultaneous optimization of the PbLaZrTi-based actuator configuration and corresponding applied light intensity for morphing beam structural shapes. A finite element formulation for multiphysics analysis of coupled opto-electro-thermo-mechanical fields in PbLaZrTi ceramics is derived and verified with the theoretical solution and the commercial software ANSYS. This element is then used to simulate beam bending shape control using the orthotropic PbLaZrTi actuators and the simultaneous optimization. In this procedure, the controlling and geometrical variables are simultaneously optimized via a hierarchical genetic algorithm. A bi-coded chromosome is proposed in a hierarchical mode, which consists of some control genes (i.e. actuator location and number) and parametric genes (i.e. applied light intensity). Whether the parametric gene is activated or not is managed by the value of the first-grade control genes. The numerical results demonstrate that the achieved beam bending shapes correlate remarkably well with the expected ones and the simultaneous optimization of photostrictive actuator locations, numbers and light intensities can result in optimal actuator layout with less PbLaZrTi actuators and irradiated light energy. The simulation results also show that the hierarchical genetic algorithm has more superior performance over the conventional real-coded genetic algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Simultaneous optimization Wireless shape control Finite element Photostrictive actuators hierarchical genetic algorithm Coupled opto-electro-thermo-mechanical fields

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Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 29卷 70-78页

作者： Mendes, Jerome Araujo, Rui Matias, Tiago Seco, Ricardo Belchior, Carlos Univ Coimbra Inst Syst & Robot ISR UC P-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn DEEC UC P-3030290 Coimbra Portugal

The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The main objective of this paper is the extraction of a FLC from data extracted from a given process while it is being manually controlled. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA), from a set of process-controlled input/output data. The algorithm is composed by a five level structure, being the first level responsible for the selection of an adequate set of input variables. The second level considers the encoding of the membership functions. The individual rules are defined on the third level. The set of rules are obtained on the fourth level, and finally, the fifth level selects the elements of the previous levels, as well as, the t-norm operator, inference engine and defuzzifier methods which constitute the FLC. To optimize the proposed method, the HGA's initial populations are obtained by an initialization algorithm. This algorithm has the main goal of providing a good initial solution for membership functions and rule based populations, enhancing the GA's tuning. Moreover, the HGA is applied to control the dissolved oxygen in an activated sludge reactor within a wastewater treatment plant. The results are presented, showing that the proposed method extracted all the parameters of the fuzzy controller, successfully controlling a nonlinear plant. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Fuzzy control hierarchical genetic algorithm Activated sludge process Fuzzy knowledge base

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Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

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International Journal of Hybrid Intelligent Systems 2021年第1-2期17卷 15-32页

作者： Melin, Patricia Sánchez, Daniela Tijuana Institute of Technology Tijuana Mexico

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set. © 2021 - IOS Press. All rights reserved.

关键词： diabetes classification hierarchical genetic algorithm optimization PIMA Indian diabetes type-1 fuzzy logic type-2 fuzzy logic

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Nanogrids: A Smart Way to Integrate Public Transportation Electric Vehicles into Smart Grids 12th

Nanogrids: A Smart Way to Integrate Public Transportation El...

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12th International Joint Conference on Computational Intelligence (IJCCI) / 12th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Ferrandino, Emanuele Capillo, Antonino Mascioli, Fabio Massimo Frattale Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9789897584756

The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their impact on the SG. As a consequence, an EV can be seen as a Nanogrid (NG) whose energy flows are optimized by an Energy Management System (EMS). In this work, an EMS for an electric boat is synthesized by a Fuzzy Inference System-hierarchical genetic algorithm (FIS-HGA). The electric boat follows cyclic routes day by day. Thus, single day training and test sets with a very short time step are chosen, with the aim of reducing the computational cost, without affecting accuracy. A convex optimization algorithm is applied for benchmark tests. Results show that the EMS succesfully performs the EV energy flows optimization. It is remarkable that the EMS achieves good performances when tested on different days than the one it has been trained on, further reducing the computational cost.

关键词： Smart Grid Electric Vehicles Bidirectional Fast Charge Renewable Energy Source Vehicle-to-Grid Grid-to-Vehicle Microgrid Nanogrid Energy Management System Fuzzy Logic Evolutionary Computing hierarchical genetic algorithm

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Optimization design based on hierarchic genetic algorithm and particles swarm algorithm

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2018年第3期12卷 217-222页

作者： Lin DengWei Jiaozuo Univ Fac Informat Engn Jiaozuo 454003 Peoples R China

For a lot of data, it is time-consuming and unpractical to get the best combination by manual tests. The genetic algorithm can make up for this shortcoming through the optimization of parameters. In this paper, the advantages of traditional similarity algorithm is studied, the time model and the trust model for further filtering are introduced, and the parameters with the combination of hierarchical genetic algorithm and particle swarm algorithm are optimized. In the collaborative filtering algorithm, genetic algorithm is improved with hierarchical algorithm, and the user model and the algorithm process are optimized using the fitness function of selection, crossover, and variation, along with the optimization of recommendation result set. In the algorithm, the global optimal parameters can be calculated with the optimization of the obtained initial data, and the accuracy of the similarity calculation can also be improved. This study does the recommendation and comparison experiment in the MovieLens Dataset, and the results show that, on the basis of obtaining the nearest neighbor user group, the mixing use of the hierarchical genetic algorithm and the particle swarm algorithm can make more improvement in the recommendation quality than that of the traditional similarity algorithm.

关键词： hierarchical genetic algorithm particle swarm algorithm similarity optimization algorithm resource recommendation

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Multi-objective optimization for modular granular neural networks applied to pattern recognition

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INFORMATION SCIENCES 2018年 460卷 594-610页

作者： Melin, Patricia Sanchez, Daniela Tijuana Inst Technol Tijuana Mexico

A new method for Modular Neural Network optimization based on a Multi-objective hierarchical genetic algorithm is proposed in this paper. The modular neural network using a granular approach and its optimization using a multi-objective hierarchical genetic algorithm provides better results than when the modular neural network is applied without a granular approach and optimization of parameters. The optimization of different parameters of the modular granular neural network architecture, such as the number of modules (sub-granules), size of the dataset for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons are performed in the proposed method. The fitness functions aim at minimizing the size of the dataset for the training phase and the error using a multi-objective approach. This method can be used in different areas of application, such as human recognition, classification problems or time series prediction. In this case the proposed method is tested with human recognition based on the face and ear biometric measures, where the proposed method aims at finding non dominated solutions based on the number of data points for training and the recognition error. Benchmark face and ear databases are used to illustrate the advantages of the proposed approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Modular neural networks Granular computing hierarchical genetic algorithm Human recognition Multi-objective optimization Pattern recognition Face recognition Ear recognition

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