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Evaluating a dendritic neuron model for wind speed forecasting

KNOWLEDGE-BASED SYSTEMS 2020年 201卷 106052-106052页

作者： Song, Zhenyu Tang, Yajiao Ji, Junkai Todo, Yuki Taizhou Univ Coll Comp Sci & Technol Taizhou 225300 Peoples R China Cent South Univ Forestry & Technol Coll Econ Changsha 410004 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

Because of the intrinsic complexity and chaotic nature of wind speed time series, an appropriate model for accurately forecasting the moving tendency is required. In this paper, we propose an evolutionary dendritic neuron model (EDNM) to carry out wind speed forecasting. The model is trained via adaptive differential evolution with the linear population size reduction (L-SHADE) algorithm. Specifically, a mutual-information-based approach and the false nearest neighbours method are used to calculate the time delay and embedding dimensions, respectively. Then, the phase space of the wind speed time series is reconstructed based on the time delay and embedding dimensions, and the characteristics are analysed. The maximum Lyapunov exponent is applied to confirm the chaotic properties of the wind speed time series. Finally, EDNM trained by L-SHADE is used to predict the wind speed for Sotavento, which is located near Galicia, Spain. This study is the first, to the best of our knowledge, to use a dendritic neuron model to implement such real-world prediction. Extensive experimental results show that the proposed EDNM can perform better than other state-of-the-art models in terms of different assessment criteria. Therefore, the proposed method has high potential for practical applications in electric power systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Neuron model Dendrite evolution algorithm Wind speed forecasting

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Mechanisms of flexible four-bar linkages: A brief review

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Materials Today: Proceedings 2021年 47卷 5570-5574页

作者： Ruby Mishra KIIT Deemed to be University Bhubaneswar 751024 India

The usages of linkage mechanisms in diverse applications, including manufacturing, are extensively appreciated. The synthesis of mechanisms for given motion and path generation has becomemore significant. The data of the kinematics of machines, thus, is a very important aspect of modern-day manufacturing. From conventional perception, kinematics' knowledge prevents many realistic mechanisms like pin deformation, joint clearances, and manufacturing inexactitudes, which cannot be overlooked. This study aims to explain the different design optimization processes and the analysis of flexible four-bar linkages for achieving a better solution. This study could be an essential step for setting the appropriate resources and optimizing the four-bar mechanism design to reduce the deviation from the desired output. The high-speed rotation of the mechanism may cause vibration and deformation in the system. So, different methods may be applied for improved four-bar mechanisms. This study provides us withprobable to resolve such conditions as precise load values regarding other vibrating elements. Like MATLAB, ADAMS, and ANSYS, the soft-computing method has been formulated to strengthen the researcher's analysis ability. These software packages may provide the improved design data in the form of displacement, deformation, and vibration frequency of the four-bar Mechanisms.

关键词： Mechanism Path generation Virtual link Optimization evolution algorithm

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Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network

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BIODATA MINING 2016年第1期9卷 18-18页

作者： Li, Ruowang Dudek, Scott M. Kim, Dokyoon Hall, Molly A. Bradford, Yuki Peissig, Peggy L. Brilliant, Murray H. Linneman, James G. McCarty, Catherine A. Bao, Le Ritchie, Marylyn D. Penn State Univ Dept Stat University Pk PA 16802 USA Penn State Univ Dept Biochem & Mol Biol Ctr Syst Genom University Pk PA 16802 USA Marshfield Clin Res Fdn Biomed Informat Res Ctr Marshfield WI USA Essentia Rural Hlth Duluth MN USA Geisinger Hlth Syst Biomed & Translat Informat Danville PA USA

Background: The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. Results: For this study, we present a new algorithm, Grammatical evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Conclusions: Developing the computational tools to explore genet

关键词： evolution algorithm Bayesian Network Genetic interactions Discriminant analysis Type 2 diabetes

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One-Dimensional Multi-Material Cutting Based on Hybrid Approximation-Genetic 56

One-Dimensional Multi-Material Cutting Based on Hybrid Appro...

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56th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Huang, Yan-Jie Chen, Chiu-Hung Chou, Jyh-Horng Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan Kao Yuan Univ Dept Informat Technol Kaohsiung Taiwan

ISBN: (纸本)9784907764579

This paper studies a hybrid method which integrates the evolution algorithm (EA) and the approximation algorithm (AA) to solve one-dimensional multi-material cutting (1-D MMC) problems. The complexity of the 1-D MMC problem is NP-hard, that is, it cannot be optimally solved by a polynomial time algorithm. Therefore, in this paper, an AA method called first fit decreasing (FFD) is first used to get an approximate solution, and then the approximate solution is evolutionarily improved by the EA method. From the experimental result, the proposed approach can successfully solve the 1-D MMC problem, and the solution quality is better than that obtained by using the improved genetic algorithm (IGA) specified in literature.

关键词： One-dimensional cutting approximation algorithm first fit decreasing evolution algorithm

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An Approach to Solving for Multi-Objective Optimization Problem with Application to Linear Motor Control Design

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CONTROL AND INTELLIGENT SYSTEMS 2005年第2期33卷 75-86页

作者： Lin, C-L. Jan, H-Y. Department of Electrical Engineering National Chung Hsing University Taichung 402 Taiwan Institute of Electrical and Information Engineering Feng Chia University Taichung 40724 Taiwan

The authors propose a new design and realization method solving for constrained multi-objective problem via an advanced evolution strategy. Requirements directly related to the H and H2 performance specifications are imposed, which reflect stability robustness and optimality. A modified PID control scheme represented only by two parameters is proposed to control a linear brushless DC motor. The design paradigm offers an effective way to implement robust solutions that covers a wide range of plant perturbations and offers excellent performance. Simulation studies and experiments are performed to verify the performance and applicability of the suggested design.

关键词： evolution algorithm multi-objective optimization robust control linear motor

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Parameter Optimization for Main Driving Speed Control System of Temper Mill

Parameter Optimization for Main Driving Speed Control System...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： Lanjun Li Xiangzhong Wang School of Electrical Engineering University of South China Hengyang 421001 College of Computer and Information Engineering Central South University of Forestry and Technology

In main driving speed control system of temper mill, there are many adjustable parameters , their tuning ranges are wide , the parameters robustness are bad , and it is hard to find a set of control parameters which operate system under optimal condition with traditional parameter tuning method. In order to solve these problems, this paper puts forward an optimal setting method of control parameters based on evolution algorithm. The searching method with proper coding form, adaption function and other evolutionary operator is efficient and robust. The method is applied to parameter optimization of main driving speed control system of temper mill, and the system performance characteristics such as the most overshoot, response speed and accuracy turn out to be optimal.

关键词： Temper mill Parameter setting evolution algorithm Optimization

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A Modified Shuffled Frog Leaping algorithm and Its Application to Short-term Hydrothermal Scheduling

A Modified Shuffled Frog Leaping Algorithm and Its Applicati...

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2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

作者： Yinghai Li Xiaohua Dong Cuimei Lv Faxing Du College of Hydraulic & Environmental Engineering China Three Gorges University Yichang Hubei 443002 China

Hydrothermal system scheduling is a large, dynamic, non-convex and nonlinear programming with complicated constraints. In order to solve such complex engineering problems, a new modified shuffled frog leaping algorithm is presented in the paper. In the proposed algorithm, a threshold judging strategy is introduced to the local evolution process to improve the search ability and convergence accuracy of standard shuffled frog leaping algorithm(SFL). Numerical experience proves validity of the modified algorithm and results show it outperforms standard SFL and PSO. Finally, the proposed algorithm is applied to a short-term hydrothermal system scheduling problem. The operation results show its feasibility and high efficiency.

关键词： evolution algorithm Shuffled Frog Leaping algorithm hydrothermal scheduling

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Research on Regional Collaborative Innovation evolution from Complex Network Perspective

Research on Regional Collaborative Innovation Evolution from...

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International Conference on Application of Information and Communication Technologies

作者： Kun Wang Duoyong Sun College of Information System and Management National University of Defense Technology Changsha Hunan P.R. China

ISBN: (纸本)9781467368575

This paper proposed a novel algorithm to analyze the evolution of regional collaborative innovation systems from the complex network perspective. At first, we illustrate the structure of collaborative networks, which contains several elements. And we found out the system could be regarded as a complex network. As to the evolution algorithm we proposed, in order to match the actual situation, each node in the network can only connect to less than a specific number of its neighbors. Specifically, for a certain node, maximum number of the final edges is only determined by its node importance. We collect some raw data from the Internet. Finally, we carried out a study to compare the evolution situation in both small and large scare network. Experimental results have demonstrated the proposed algorithm is valid. Simulation results are consistent well with the realities of situation. Moreover, some beneficial conclusions can be summarized from our paper.

关键词： Regional collaborative innovation Complex network evolution algorithm Statistical yearbook

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A Cultural algorithm Based on Multilayer Belief Spaces and Its Application in Neural Network Fault Classifier

A Cultural Algorithm Based on Multilayer Belief Spaces and I...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.10

作者： Haiyan Huang Mandan Liu Xingsheng Gu Department of Automation East China University of Science and Technology Shanghai China

This paper presents a Cultural algorithm (CA) based on multilayer belief spaces that selects the best belief space from the multilayer belief spaces to guide the search of population space. The selected best belief space exploits knowledge extracted during the search to improve the performance of an evolutionary algorithm. We integrate cultural algorithm based on multilayer belief spaces and evolutionary programming to develop a more efficient algorithm, called CMAEP used for global optimization, and the knowledge sources in the belief spaces of the cultural algorithm are specifically designed. The tests of optimizing benchmark functions show the approach is valid and effective. Then we apply it to optimize the weights and thresholds of BP network as fault classifier to discriminate chemical process steady faults. The simulations on Tennessee Eastman process (TEP) show CMAEP can effectively escape from local optima to find the global optimal value comparing with other optimization methods and the optimized BP network based on CMAEP can obtain a satisfied diagnosis result. It is successful to apply the algorithm in Neural Network fault classifier.

关键词： evolution algorithm Cultural algorithm Multilayer Belief Spaces Neural Network

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Parameter Optimization for Main Driving Speed Control System of Temper Mill

Parameter Optimization for Main Driving Speed Control System...

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2010 Chinese Control and Decision Conference

作者： Lanjun Li~1,Xiangzhong Wang~2 1.School of Electrical Engineering,University of South China,Hengyang,421001 2.College of Computer and Information Engineering, Central South University of Forestry and Technology,Changsha,410014

In main driving speed control system of temper mill,there are many adjustable parameters,their tuning ranges are wide,the parameters robustness are bad,and it is hard to find a set of control parameters which operate system under optimal condition with traditional parameter tuning *** order to solve these problems,this paper puts forward an optimal setting method of control parameters based on evolution *** searching method with proper coding form,adaption function and other evolutionary operator is efficient and *** method is applied to parameter optimization of main driving speed control system of temper mill,and the system performance characteristics such as the most overshoot,response speed and accuracy turn out to be optimal.

关键词： Temper mill Parameter setting evolution algorithm Optimization

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