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A Review of Computing with Spiking Neural Networks

Computers, Materials & Continua 2024年第3期78卷 2909-2939页

作者： Jiadong Wu Yinan Wang Zhiwei Li Lun Lu Qingjiang Li College of Electronic Science and Technology National University of Defense TechnologyChangsha410073China

Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so *** neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning *** the early stage of development,its poor performance hindered the application of SNNs in real-world *** recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant *** there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research *** from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of ***,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development *** the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also *** research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of *** advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing

关键词： Spiking neural networks neural networks brain-like computing artificial intelligence learning algorithm

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Vibration analysis of drilling machine using proposed artificial neural network predictors

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2012年第10期26卷 3037-3046页

作者： Eski, Ikbal Erciyes Univ Fac Engn Mechatron Engn Dept TR-38039 Kayseri Turkey

Small tolerances are very important factors for drilling machines. Due to the mechanical friction on their moving parts, it is necessary to predict vibration effects. This investigation is focused on design of robust neural network predictors for analyzing vibration effects on moving parts of drilling machines. The research is divided into two parts;the first part is experimental investigation, the second part is simulation analysis with neural networks. Therefore, a real time drilling machine is used for vibrations under working conditions. The measured real vibration parameters are analyzed with neural network. As a result, simulation approaches show that radial basis neural network has superior performance to adapt real time parameters of drilling machines.

关键词： Artificial neural network Drilling machine learning algorithm Vibration analyses

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Artificial neural network approach to the fuzzy Abel integral equation problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期27卷 83-91页

作者： Jafarian, Ahmad Nia, Safa Measoomy Islamic Azad Univ Dept Math Urmia Branch Orumiyeh Iran

Artificial neural networks (ANNs) have a large appeal to many researchers due to their feature to simulate and solve different kinds of problems that do not have algorithmic solutions. The outlined in this paper is an efficient and robust collocation method based on the ANNs and Bernstein polynomials intended for the fuzzy Abel integral equation problem. To do this, first truncated Bernstein-series polynomial of the solution function is substituted in parametric form of the given fuzzy problem. Then an architecture of ANNs namely the feed-back neural nets is designed to determine values for the unknown coefficients. Eventually, the proposed method is implemented on some numerical examples, and also is compared with an usual and classical technique.

关键词： Fuzzy Abel integral equation bivariate Bernstein polynomial feed-back neural network cost function learning algorithm

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A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation

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APPLIED SOFT COMPUTING 2020年 91卷 106241-106241页

作者： Mohammadzadeh, Ardashir Kumbasar, Tufan Univ Bonab Elect Engn Dept Bonab Iran Istanbul Tech Univ Dept Control & Automat Engn Istanbul Turkey

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman's model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared with the other T2 fuzzy and well-known model predictive controllers. The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system. (C) 2020 Elsevier B.V. All rights reserved.

关键词： General Type-2 Fuzzy Logic Controller Type-1 diabetes learning algorithm Fractional-order Stability analysis

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Tissue classification in magnetic resonance images through the hybrid approach of Michigan and Pittsburg genetic algorithm

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APPLIED SOFT COMPUTING 2011年第4期11卷 3476-3484页

作者： Mehta, Shashi Bhushan Chaudhury, Santanu Bhattacharyya, Asok Jena, Amarnath Delhi Coll Engn Delhi 110042 India Indian Inst Technol Delhi 110016 India Rajiv Gandhi Canc Inst & Res Ctr MRI Ctr Chief MRI Delhi 110085 India

Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Segmentation Pattern analysis Fuzzy rules FRBS learning algorithm Pittsburg Michigan Genetic algorithm Magnetic resonance imaging

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Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction

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ENERGY REPORTS 2021年 7卷 8115-8127页

作者： Cao, Yan Raise, Amir Mohammadzadeh, Ardashir Rathinasamy, Sakthivel Band, Shahab S. Mosavi, Amirhosein Xian Technol Univ Sch Mech Engn Xian 710021 Peoples R China Xian Technol Univ Dept Mech Engn Xian Shaanxi Peoples R China Univ Bonab Dept Elect Engn Bonab Iran Bharathiar Univ Dept Appl Math Coimbatore 641046 Tamil Nadu India Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ RdSect 3 Touliu Yunlin 64002 Taiwan Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary

A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme. (C) 2021 Published by Elsevier Ltd.

关键词： Fuzzy logic Renewable energy learning algorithm Deep learning Solar energy Wind turbines

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A novel optimal PID controller autotuning design based on the SLP algorithm

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EXPERT SYSTEMS 2020年第2期37卷 e12489-e12489页

作者： Pongfai, Jirapun Su, Xiaojie Zhang, Huiyan Assawinchaichote, Wudhichai King Mongkuts Univ Technol Thonburi Fac Engn Dept Elect & Telecommun Engn Bangkok Thailand Chongqing Univ Coll Automat Chongqing Peoples R China Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing Peoples R China

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the "swarm learning process" (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder-Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

关键词： artificial intelligence control system learning algorithm optimization particle swarm PID controller

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Robots That Think Fast and Slow: An Example of Throwing the Ball Into the Basket

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IEEE ACCESS 2016年 4卷 5052-5064页

作者： Li, Tzuu-Hseng S. Kuo, Ping-Huan Ho, Ya-Fang Liu, Chin-Yin Yu, Ting-Chieh Ye, Yan-Ting Chang, Chien-Yu Chen, Guan-Yu Chien, Chih-Wei Chen, Wei-Chung Wu, Li-Fan Fang, Nien-Chu Natl Cheng Kung Univ Tainan 701 Taiwan

Can a robot think like a human being? Scientists in recent years have been trying to achieve this dream, and we are also committed to this same goal. In this paper, we use an example of throwing the ball into the basket to make the robots process with human-like thinking behavior. Such thinking behavior adopted in this paper is divided into two modes: fast and slow. The fast mode belongs to the intuitional reaction, and the slow mode represents the complicated cogitation in human brain. This fascinating human thinking concept is inspired by the book, Thinking, Fast and Slow, which explains the process of the human brain. In addition, the psychology theories proposed in this book are also adopted to realize the thinking algorithms, and our experiments verify that the thinking mode of human beings is reasonable and effective in robots.

关键词： Anchoring effect fast and slow systems FIRA humanoid robot learning algorithm peak-end rule psychology

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Resource Optimization for Device-to-Device and Small Cell Uplink Communications Underlaying Cellular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第2期67卷 1187-1201页

作者： Dai, Haibo Huang, Yongming Zhao, Rui Wang, Jiaheng Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Nanjing 210003 Jiangsu Peoples R China Huaqiao Univ Xiamen Key Lab Mobile Multimedia Commun Xiamen 361021 Peoples R China

This paper investigates the joint power control and subchannel allocation problem for device-to-device and small cell uplink communications in a cellular network, with the aim of improving the network throughput. For this throughput maximization problem, we leverage a game-theoretic learning approach to solve it. However, there is an intractable issue for the feasible region of the optimization problem, which is intermixed with the integer nature. To tackle this problem, we first present the closed-form expressions of the optimal power under different scenarios by presetting the subchannel allocation profile. Based on the optimal power control, we then formulate the subchannel allocation problem as a game theoretical framework, and define a welfare function which has the same optimum as the optimization objective. To optimize the welfare function, a distributed trial and error learning algorithm is proposed to converge to a stochastically stable state. Since the achieved stable state cannot guarantee to be the optimal solution, we reformulate this problem as an exact potential game model and propose another distributed learning algorithm to find a better Nash equilibrium which is the global optimal solution (or a superior solution). Moreover, to accelerate the convergence, these two algorithms are modified by getting rid of these unavailable strategic profiles. Finally, numerical results verify the effectiveness of the proposed schemes.

关键词： Device-to-device communication game theory heterogeneous network learning algorithm

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Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: Case of grammatical inference

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SWARM AND EVOLUTIONARY COMPUTATION 2016年 31卷 11-23页

作者： Pandey, Hari Mohan Chaudhary, Ankit Mehrotra, Deepti Kendall, Graham Amity Univ Uttar Pradesh Dept Comp Sci & Engn Sect 125 Noida India Truman State Univ Dept Comp Sci Kirksville MO USA Amity Univ Amity Sch Engn & Technol Sect 125 Noida India Univ Nottigham Malaysia CampusJalan Borga Semenyih 43500 Selangor Darul Malaysia

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length pr

关键词： Bit-masking oriented data structure Context free grammar Genetic algorithm Grammar induction learning algorithm Minimum description length principle

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