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Nonlinear system identification and control using a real-coded genetic algorithm

APPLIED MATHEMATICAL MODELLING 2007年第3期31卷 541-550页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

A real-coded genetic algorithm (GA) applied to the system identification and control for a class of nonlinear systems is proposed in this paper. It is well known that GA is a globally optimal method motivated from natural evolutionary concepts. For solving a given optimization problem, there are two different kinds of GA operations: binary coding and real coding. In general, a real-coded GA is more suitable and convenient to deal with most practical engineering applications. In this paper, in the beginning we attempt to utilize a real-coded GA to identify the unknown system which its structure is assumed to be known previously. Next, according to the estimated system model an optimal off-line PID controller is optimally solved by also using the real-coded GA. Two simulated examples are finally given to demonstrate the effectiveness of the proposed method. (c) 2005 Elsevier Inc. All rights reserved.

关键词： nonlinear system identification parameters estimation PID controller real-coded genetic algorithm optimization problem

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genetic algorithm and M-estimator based robust sequential estimation of parameters of nonlinear sinusoidal signals

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2011年第7期16卷 2796-2809页

作者： Mitra, Sharmishtha Mitra, Amit Kundu, Debasis Indian Inst Technol Dept Math & Stat Kanpur 208016 Uttar Pradesh India

Estimation of parameters of nonlinear superimposed sinusoidal signals is an important problem in digital signal processing. In this paper, we consider the problem of estimation of parameters of real valued sinusoidal signals. We propose a real-coded genetic algorithm based robust sequential estimation procedure for estimation of signal parameters. The proposed sequential method is based on elitist generational genetic algorithm and robust M-estimation techniques. The method is particularly useful when there is a large number of superimposed sinusoidal components present in the observed signal and is robust with respect to presence of outliers in the data and impulsive heavy tail noise distributions. Simulations studies and real life signal analysis are performed to ascertain the performance of the proposed sequential procedure. It is observed that the proposed methods perform better than the usual non-robust methods of estimation. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Elitism Generational genetic algorithm M-estimator Nonlinear least squares Periodogram estimates real-coded genetic algorithm Robust estimation Sequential estimation Speech signals

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On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics

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APPLIED MATHEMATICS AND COMPUTATION 2011年第22期217卷 8997-9008页

作者： Chandra, Rohitash Rolland, Luc Fiji Natl Univ Coll Business Hospitality & Tourism Studies Dept Comp Sci & Informat Syst Suva Fiji Toros Univ Fac Engn Mersin Turkey

Hybrid metaheuristics have been applied with success in solving many real-world problems. This work introduces hybrid metaheuristics to the field of kinematics problem, in particular, for solving the forward kinematics of the 3RPR parallel manipulator. It implements a combination of genetic algorithms and simulated annealing into two popular hybrid metaheuristic techniques. They are combined as teamwork and relay collaborative hybrid metaheuristics and compared to the performance of genetic algorithms and simulated annealing alone. The results show that the meta-heuristic approaches give robust and high quality solutions. genetic algorithms and teamwork collaborative metaheuristics showed better performance than simulated annealing and relay collaborative metaheuristics. The given metaheuristic methods obtain all the unique solutions and comparisons with algebraic methods show promising results. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Forward kinematics Planar parallel manipulators real-coded genetic algorithm Simulated annealing Teamwork collaborative hybrid metaheuristics Relay collaborative hybrid metaheuristics

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Novel genetic algorithm for short-term scheduling of sequence dependent changeovers in multiproduct polymer plants

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COMPUTERS & CHEMICAL ENGINEERING 2011年第12期35卷 2945-2959页

作者： Ramteke, Manojkumar Srinivasan, Rajagopalan Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117576 Singapore ASTAR Inst Chem & Engn Sci Jurong Isl 627833 Singapore

Polymer plants generally operate to produce different grades of product from the same reactor. Such systems commonly require short-term scheduling to meet market demand. One important requirement in continuous-time scheduling of such systems is to satisfy a variety of constraints, including identifying feasible sequences of the predecessor and successor jobs to effectively handle changeovers. In this study, a new genetic algorithm (GA) is proposed to solve such job sequencing problems. The proposed GA uses real-coded chromosome to represent job orders and their sequences in the schedule. The novelty is that the representation ensures that all constraints are satisfied a priori, except the sequence constraint which is handled by penalizing violations. Three important problems relevant to polymer industry are solved to obtain optimal schedules. The first deals with the sequencing constraint between individual product orders, the second with sequencing constraint between groups of product orders, while the third incorporates batching with scheduling. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Grades Multi-objective optimization real-coded genetic algorithm Linear programming Stochastic modeling

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A Physics Modeling of Butterfly's Flight Control by GA and ANN and Its Over-evolution Problem

A Physics Modeling of Butterfly's Flight Control by GA and A...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Ooe, R. Suzuki, I. Yamamoto, M. Furukawa, M. Hokkaido Univ Grad Sch Informat & Sci Sapporo Hokkaido Japan

ISBN: (纸本)9784990288051

We describe a simple physics model of a butterfly and an approach to its flight control by the genetic algorithm (GA) and the artificial neural network (ANN). A physics model consists of two kinetic equations which are led by a simplification of fluid force. A butterfly's flight is controlled by an ANN. The GA optimizes weights of the ANN for the suitable flight. This approach resulted in the flight which is obtained by maximizing the prepared fitness. However, the optimized ANN did not have a generality and a butterfly fell down in changing the initial height. A transition of fitness throughout processes of evolution showed that too much optimization tends to break the generality. We call this phenomenon "over-evolution". Changing conditions of experiments prevented the over-evolution.

关键词： Physical simulation Drag force Butterfly real-coded genetic algorithm Artificial neural network

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An improved real-coded genetic algorithm for parameters estimation of nonlinear systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2006年第1期20卷 236-246页

作者： Chang, WD Shute Univ Dept Comp & Commun Kaohsiung 824 Taiwan

This paper presents a searching method for parameters estimation of nonlinear system by using a modified real-coded genetic algorithm (GA). It is well known that GA method is an optimal or nearoptimal search technique borrowing the concepts from biological evolutionary theory. The ordinary form of GA used for solving a given optimization problem is a binary encoding during operating procedures. However, in the real applications a real-valued encoding is usually used and is easy to directly implement the programming operations. Thus, in this paper we develop a multi-crossover real-coded GA and utilize it to estimate the parameters of nonlinear process systems, even though those have the term of the time delay or are not linear in the parameters. The effectiveness of the proposed algorithms is compared with different evolutionary algorithms. Simulation results of two kinds of process systems will be illustrated to show that the more accurate estimations can be achieved by using our proposed method. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： system identification parameters estimation multiple crossover real-coded genetic algorithm

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A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm

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SENSORS AND ACTUATORS A-PHYSICAL 2006年第2期132卷 643-650页

作者： Ha, Jih-Lian Kung, Ying-Shieh Fung, Rong-Fong Hsien, Shao-Chien Natl Kaohsiung First Univ Sci & Technol Dept Mech & Automat Engn Kaohsiung 824 Taiwan So Taiwan Univ Technol Dept Elect Engn Tainan 710 Hsien Taiwan Far E Coll Dept Mech Engn Tainan 744 Taiwan

The main objective of this study is to identify the piezoelectric actuator (PA) with hysteresis, which is described by Bouc-Wen model. The identification method based on real-coded genetic algorithm (RGA) has the advantages not only to, identify the parameters of the hysteresis model but also the mass, stiffness, damping coefficient and piezoelectric coefficient of the PA simultaneously. Furthermore, the selection of fitness functions in the RGA is the key point to influence the final identified results. In this paper, three types of fitness functions are designed to identify the hysteretic PA and their differences are discussed. The numerical simulations and experimental results demonstrate that the identification method based on the RGA with suitable fitness functions is feasible. (c) 2006 Elsevier B.V. All rights reserved.

关键词： identification piezoelectric actuator hysteretic model real-coded genetic algorithm

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Evolving Search Spaces to Emphasize the Performance Difference of real-coded Crossovers Using genetic Programming

Evolving Search Spaces to Emphasize the Performance Differen...

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2010 IEEE World Congress on Computational Intelligence

作者： Shirakawa, Shinichi Yata, Noriko Nagao, Tomoharu Yokohama Natl Univ Grad Sch Environm & Informat Sci Hodogaya Ku Kanagawa 2408501 Japan

ISBN: (纸本)9781424481262

When we evaluate the search performance of an evolutionary computation (EC) technique, we usually apply it to typical benchmark functions and evaluate its performance in comparison to other techniques. In experiments on limited benchmark functions, it can be difficult to understand the features of each technique. In this paper, the search spaces that emphasize the performance difference of EC techniques are evolved by Cartesian genetic programming. We focus on a real-coded genetic algorithm, which is a type of genetic algorithm that has a real-valued vector as a chromosome. In particular, we generate search spaces using the performance difference of real-coded crossovers. In the experiments, we evolve the search spaces using the combination of three types of real-coded crossovers. As a result of our experiments, the search spaces that exhibit the largest performance difference of two crossovers are generated for all the combinations.

关键词： Cartesian genetic programming chromosome evolving search spaces genetic algorithms performance difference real-coded crossovers real-coded genetic algorithm real-valued vector search problems

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Self-tuning Fuzzy Logic Control of Greenhouse Temperature using real-coded genetic algorithm

Self-tuning Fuzzy Logic Control of Greenhouse Temperature us...

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9th International Conference on Control, Automation, Robotics and Vision

作者： Xu, Fang Chen, Jiaoliao Zhang, Libin Zhan, Hongwu Zhejiang Univ Technol MOE Key Lab Mech Manufacture & Automat Hangzhou 310032 Peoples R China

ISBN: (纸本)9781424403417

The greenhouse temperature model is built based on the balance of the energy. A new real-coded genetic algorithm (GA) for self-tuning fuzzy logic control (FLQ of greenhouse temperature is proposed, in which, an arithmetical crossover operator, a ranldng-based reproduction operator and a non-uniform mutation operator are adopted. The Gaussian input membership functions for the error and the change-in-error of the temperature of FLC is optimized by GA in terms of the root-mean-square error (RMSE) with setpoint and input energy. Compared with the basic fuzzy control, the tuned FLC gives better performance in terms of improving control precision and saving energy.

关键词： greenhouse temperature fuzzy logic control real-coded genetic algorithm self-tuning

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A hybrid real-coded genetic algorithm with forgetting and its applications

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International Congress Series 2006年 1291卷 189-192页

作者： Zhang, H. Ishikawa, M. Department of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu 808-0196 2-4 Hibikino Wakamatsu Japan

This paper proposes a hybrid real-coded genetic algorithm with forgetting for improving the generalization ability of classification models. A crucial idea here is the introduction of structural learning with forgetting into a hybrid real-coded genetic algorithm. The proposed method has two advantages: (1) finding near optimal classification models efficiently by a hybrid technique and (2) improving the generalization ability of the resulting classification models by the forgetting technique. Applications of the proposed method to an iris classification problem well demonstrate its effectiveness. Our results indicate that it has not only high learning performance for training data, but also high generalization ability for the test data compared with conventional algorithms such as backpropagation and structural learning with forgetting. © 2006 Elsevier B.V. All rights reserved.

关键词： Local search Multilayer preceptron real-coded genetic algorithm Structural learning with forgetting

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