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A Proposal of genetic Operations for BSIM Parameter Extraction Using real-coded genetic algorithm

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第8期15卷 1131-1138页

作者： Nishiba, Ai Kawanaka, Hiroharu Takase, Haruhiko Tsuruoka, Shinji Mie Univ Grad Sch Engn 1577 Kurima Machiya Tsu Mie 5148507 Japan Mie Univ Grad Sch Reg Innovat Studies Tsu Mie 5148507 Japan

This paper discusses genetic operations and their effects on evolution of GA in BSIM parameter extraction problems. Generally, real-coded genetic algorithm (RCGA) using Simplex Crossover (SPX) is often employed to extract BSIM parameter sets. BSIM parameters, however, have recommended operating ranges. There are regarded as constraints, thus all extracted parameters have to be satisfied them. In many cases, when the number of parameters becomes large, the conventional methods generate a lot of infeasible solutions because SPX makes offspring on the simplex plane expanded by epsilon parameter. This makes search efficiency of GA reduce drastically. Because of these factors, we propose genetic operations considering the constraints to prevent reduction of search efficiency of GA. In this paper, some experiments using actual static characteristic curves of MOS-FET were conducted to validate the proposed method. This paper also discussed the effectiveness of the proposed method.

关键词： BSIM parameter extraction problem real-coded genetic algorithm simplex crossover genetic operations constraints

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real coded genetic algorithm FOR STOCHASTIC HYDROTHERMAL GENERATION SCHEDULING

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Journal of Systems Science and Systems Engineering 2011年第1期20卷 87-109页

作者： Jarnail S.DHILLON J.S.DHILLON D.P.KOTHARI G.Z.S.College of Engineering and Technology Sant Longowal Institute of Engineering and Technology Vindhya Institute of Technology and Science

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

关键词： Stochastic multi-objective optimization real-coded genetic algorithm fuzzy set economicload dispatch

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Integrated real-coded genetic algorithm and Particle Swarm Optimization for Solving Constrained Global Optimization Problems

Integrated Real-Coded Genetic Algorithm and Particle Swarm O...

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Advances in Information Technology and Education

作者： Jui-Yu Wu Department of Business Administration Lunghwa University of Science and Technology

This work presents an integrated real-coded genetic algorithm and particle swarm optimization with a penalty function method(RGA-PSO) to solve constrained global optimization(CGO) problems. An outer RGA is used to optimize the parameter settings of an inner PSO algorithm, which includes the constriction coefficient, cognitive parameter, social parameter, penalty parameter and mutation probability. To increase the diversity of particles, the inner PSO method uses a multi-non-uniform operator. Performance of the proposed RGA-PSO algorithm is evaluated using a set of benchmark CGO problems. Additionally, this work compares the numerical results obtained using the proposed RGA-PSO algorithm with those of GAs and artificial immune algorithms(AIAs). Experimental results indicate that the proposed RGA-PSO algorithm converges to a global optimum solution to a CGO problem, and that the performance of the proposed RGA-PSO algorithm is competitive with those of GAs and AIAs. Although a hybrid method increases the complexity of an algorithm, the proposed RGA-PSO algorithm can obtain the optimum parameter settings for the inner PSO algorithm by using the outer RGA and can find a global optimum solution to a CGO problem by using the inner PSO algorithm, simultaneously. Hence, the proposed RGA-PSO algorithm can be considered as an alternative stochastic global optimization method for solving CGO problems.

关键词： real-coded genetic algorithm particle swarm optimization nonlinear programming constrained global optimization

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Dental Caries Degree Detection based on Fuzzy Cognitive Maps and genetic algorithm

Dental Caries Degree Detection based on Fuzzy Cognitive Maps...

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Iranian Conference on Electrical Engineering

作者： Arman Haghanifar Abdollah Amirkhani Mohammad R. Mosavi Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran

Dental caries is the most common infectious disease in humans, which affects 97% of the population in their lifetimes. In this article, fuzzy cognitive maps (FCMs) are applied to classify patients in terms of their personal risk of dental caries. Two approaches are discussed for developing FCMs. At first, causal relations between concepts and their weights are defined based on the domain experts. Then, these causal weights are determined by means of the real-coded genetic algorithm (RCGA) and historical data, without the intervention of experts. These two FCMs have been compared by categorizing 86 sample patients into two classes: 'carious' and 'healthy'. Although the conventional FCM has successfully categorized 67.44% of the cases in correct classes, the KCGA based FCM hits the 83.72% correct classification rate. Results show the efficiency of RCGA based FCM in comparison with the conventional FCM.

关键词： Fuzzy cognitive maps Dental caries real-coded genetic algorithm Patient classification

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A Two-stage Traffic Sensor Location Method for Low-volume Road Incident Detection 13

A Two-stage Traffic Sensor Location Method for Low-volume Ro...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Xiao-Feng Zhang, Ji-Sheng Gao, Ting-Ting Guan, Zhi-Wei Song, Yu-Qing Tianjin Univ Technol & Educ Sch Automot & Transportat Tianjin 300222 Peoples R China Minist Commun Key Lab Intelligent Transportat Syst Technol Beijing 100088 Peoples R China

ISBN: (纸本)9781538621653

Low-volume road always lied at the area with few inhabitants and few traffic monitoring equipment, thus it's difficult to detect traffic incident and take traffic rescue in a timely manner. However, most research focused on high-volume road traffic sensor location for OD matrix estimation, travel time estimation and traffic volume estimation, not aiming to lowvolume road for incident detection. Firstly, a two-stage traffic sensor location method was proposed for low-volume road incident detection, with the optimization objective of maximizing incident detection rate. At the first stage, high-resolution camera Automatic Vehicle Identification (AVI) was used to monitor closed road segments, at the second stage, video was used to monitor both the closed road segments and intersection segments. Then, reliability theory was adopted to model the incident detection rate of AVI and video parallel detection system. Next, a real-coded genetic algorithm was proposed to optimize the traffic sensor location problem. Finally, a case study was conducted and the case study result shows that incident detection rate of the first and second stage increase by 36.39% and 7.68% respectively, this demonstrates that the proposed method is effective.

关键词： low-volume road incident detection traffic sensor location optimization real-coded genetic algorithm

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Evolutionary optimized discrete Tchebichef moments for image compression applications

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第4期24卷 3321-3334页

作者： Ragamathunisa Begum, Abdul Hameed Manimegali, Duraisamy Abudhahir, Ameerbasha Baskar, Subramanian Natl Engn Coll Dept Informat Technol Kovilpatti Tamil Nadu India Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai Tamil Nadu India

Evolutionary optimized coefficients of discrete orthogonal Tchebichef moment transform (TMT) are utilized in this study to ameliorate the quality of the traditional moments-based image compression methods. Most of the existing methods compute moment-transform coefficients for the input image and then select the coefficients sequentially downward to a certain order based on the desired compression ratio. However, the proposed method divides the input image into nonoverlapping square blocks of specific size in order to circumvent the problem of numerical instability and then computes the TMT coefficients for each block. In this work, a real-coded genetic algorithm is employed to optimize the TMT coefficients of each block, which produces reconstructed images of better quality for the desired compression ratio. Here the optimization is carried out by minimizing the mean square error function. Standard test images of two different sizes (128 x 128 and 256 x 256) have been subjected to the proposed compression method for the block sizes (4 x 4 and 8 x 8) in order to assess its performance. The results reveal that the proposed real-coded genetic algorithm-based method outperforms others, namely the conventional sequential selection method and simple random optimization method, for the chosen input images in terms of the task of compression.

关键词： Image compression Tchebichef moments evolutionary optimization real-coded genetic algorithm

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Designing an optimized model-free controller for improved motion tracking by rugged electrohydraulic system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2016年第5期230卷 385-396页

作者： Mandal, Pranibesh Sarkar, Bikash K. Saha, Rana Mookherjee, Saikat Sanyal, Dipankar Jadavpur Univ Dept Mech Engn Kolkata India NIT Meghalaya Dept Mech Engn Shillong 793003 Meghalaya India

Electrohydraulic actuation systems with proportional valves and industry-grade cylinders have widespread use due to their low cost and ease of maintenance. Achieving good tracking of output position of the piston in such a system by a proper controller design is the objective of this study. Due to various parameter uncertainties and complex nonlinearities like piston friction, valve deadband and flow characteristics, the variation of oil property with working temperature and air entrapment volume, the implementation of a model-based control becomes quite difficult for such systems. A notable contribution of this work is to couple a model-free fuzzy controller with a feedforward controller with all the parameters estimated by a real-coded genetic algorithm. Also, the optimizer has been used for identifying an appropriate fuzzy structure that is easy to implement in real time. A comprehensive experimental study has revealed the best fuzzy structure to be achieved through a Gaussian fuzzification of linear combination of the position and velocity errors as the input and singleton membership function for the output voltage actuating the proportional valve. Excellent responses with good disturbance rejection capability and energy efficiency have been achieved. These responses have emerged superior to those achieved in a similar system with different controller structures and in systems with much costlier components.

关键词： Deadband friction fuzzy feedforward bias real-time control real-coded genetic algorithm

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Mathematical modelling and two-stage identification for a motor-toggle mechanism with clamping effect

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APPLIED MATHEMATICAL MODELLING 2016年第19-20期40卷 8660-8670页

作者： Hsu, Yi-Lung Fung, Rong-Fong Natl Kaohsiung First Univ Sci & Technol Dept Mech & Automat Engn 1 Univ Rd Kaohsiung 824 Taiwan

The effect of clamping forces on a motor-toggle mechanism is dynamically modeled in this paper, and the clamping unit is presented by a simple spring-damper model. Based on real practical applications, the impact force model (IFM) associated with clamping effectiveness must be considered during the process of clamping. A two-stage identification method is also needed to develop for validating dynamic responses of the unclamping and clamping motions. The real-coded genetic algorithm (RGA) is employed to obtain system's parameters by two different fitness functions. The RGA feasibility is verified by the assigned parameters in numerical simulations, and then applied to the experimental physical model. Finally, numerical simulations and experimental results showed good agreement, and the proposed two-stage identification method described in this paper is found to be both better and necessary for the mechatronic system with a clamping unit. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Clamping unit Impact force model real-coded genetic algorithm Two-stage identification

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Parameter Estimation of a Skill Evaluation Model

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2017年第3期4卷 205-208页

作者： Kawada, Kazuo Tamai, Teruyuki Yamamoto, Toru Hiroshima Univ Dept Technol & Informat Educ Higashihiroshima Hiroshima 7398524 Japan Matsuyama City Kamogawa Jr High Sch 2-7-19 Kamogawa Matsuyama Ehime 7918004 Japan Hiroshima Univ Div Elect Syst & Math Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan

In this study, the aim is to construct a "teacher-student model" for optimal skill acquisition. To support the optimal skill acquisition, modeling and estimation of an individual learning process are very important. The first-order system with time delay is introduced as an individual learning process model based the control engineering approach. These system parameters included in the skill evaluation model are estimated by using a real-coded genetic algorithm. In order to evaluate the effectiveness of the proposed scheme, it is employed for the parts classification task.

关键词： Skill evaluation model Teacher-student model Learning process First-order system real-coded genetic algorithm Parameter estimation

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Optimal Control Solving of Polymer Flooding Based on real-coded genetic algorithm

Optimal Control Solving of Polymer Flooding Based on Real-co...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Lei, Yang Li, Shurong Zhang, Xiaodong China Univ Petr Coll Informat & Control Engn Dongying Shandong Peoples R China

ISBN: (纸本)9781424467129

This paper researches the optimization of injection strategies of polymer flooding in oil recovery. An optimal control problem (OCP) of a distributed parameter system (DPS) is established, in which the functional of performance index is profit maximum and the governing equations are the fluid equations in porous media. The control variables are chosen as the polymer concentrations and the switching time of a slug. The constraint conditions include boundary constraints and other inequality constraints. The method based on real-coded genetic algorithm (RGA) is presented for numerically solving the OCP. Finally, an example of the OCP for polymer flooding is exposed and the results show the effectiveness of the RGA method.

关键词： Optimal control Polymer flooding real-coded genetic algorithm

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