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International Joint Conference on Neural Networks

作者： Kim, Jeong-seob Jung, Seul Chungnam Natl Univ Mechatron Engn Dept Taejon 305764 South Korea

ISBN: (纸本)9781424418206

This article presents the hardware implementation of the Radial Basis Function (RBF) neural network whose internal weights are updated in the real-time fashion by the back-propagation algorithm. The floating-point processor is designed on a field programmable gat array (FPGA) chip to execute nonlinear functions required in the parallel processing calculation of the back-propagation algorithm. The performance of the on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning program written in the MATLAB software under the same condition to check the feasibility of the implemented neural chip. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the nonlinear XOR logic.

关键词： RBF neural network back-propagation algorithm floating point processor FPGA

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A digital background calibration scheme for non-linearity of SAR ADC using back-propagation algorithm

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MICROELECTRONICS JOURNAL 2021年 114卷 105113-105113页

作者： Lu, Hao-wei Yu, Xiao-Peng Wang, Si-Qi Liu, Yu-Yan Huang, Zhen-Yan Lu, Zheng-Hao Yeo, Kiat-Seng Chen, Jer-Ming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Soochow Univ Suzhou 215006 Peoples R China Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore

A new digital background calibration scheme for non-linearity of successive approximation register (SAR) analogto-digital convertor (ADC) is presented. Since non-linearity of high resolution SAR ADC is mainly caused by the difference between the real and ideal weights of capacitor digital-to-analog convertor (CDAC) with respect to the normalized-full-scale, calibration of weights of more significant bits is necessary when the resolution of SAR ADC comes to more than 12 bits. By using back-propagation algorithm to train the normalized real weight of more significant bits (MSBs) in neural network without any change in SAR ADC circuit design, the calibration table for each bit is implemented and updated in the digital domain without interrupting normal ADC process, which is used to correct the raw SAR code in the background to improve the performance of ADC, which is suitable for some detection applications in particular circumstances. In MATLAB simulation, the signal to noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) of a 14-bit with 1-bit redundancy SAR ADC model are improved to 85.59 dB and 97.27 dB from 56.65 dB to 77.07 dB using the proposed calibration scheme, at a standard deviation of a unit capacitor of 2%.

关键词： Successive approximation register(SAR) Analog-to-digital converter(ADC) Capacitor digital-to-analog convertor (CDAC) Linearity calibration Digital background calibration back-propagation algorithm Neural network

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Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA III coupled with support vector machine and back-propagation algorithm

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ENERGY 2023年第PartA期262卷

作者： Li, Yangyang Zhou, Shi Liu, Jingping Tong, Ji Dang, Jian Yang, Fuyuan Ouyang, Minggao Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China

This paper presents an optimization method using Non-dominated Sorting Genetic algorithm (NSGA) III to drive support vector machine (SVM). In the NSGA III algorithm, brake specific fuel consumption (BSFC), NOx and CO2 are optimized by changing the engine control parameters including spark angle, VVT-I (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA III algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA III fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO2 and CO (g/kW.h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multiobjective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model.

关键词： Atkinson cycle gasoline engine NSGA III algorithm Support vector machine algorithm back-propagation algorithm Enhancing training Digital twins

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Learning matte extraction in green screen images with MLP classifiers and the back-propagation algorithm

Learning matte extraction in green screen images with MLP cl...

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International Conference on Electronics, Communications and Computers

作者： Roberto Rosas-Romero Omar Lopez-Rincon E. David Rojas Nestor-Paul Jacobo Universidad de las Americas Puebla

ISBN: (纸本)9781509000807

This paper considers the problem of automatically extracting a foreground element with its alpha matte in green screen images by training a multi-layer perceptron (MLP) with the back-propagation algorithm. The classifier learns to identify green backgrounds, foreground object contours, and the corresponding alpha values for subsequent digital compositing. We developed our own dataset to train and test the MLP for alpha matte extraction. To speed up the generation of the training set, a second method for semi-automatic alpha matte extraction is proposed. Different experiments show that automatic matte extraction, based on the MLP, generates high-quality matting visually and it is also shown that results depend on the training and the architecture of the classifier. To the best of our knowledge, this is the first effort that applies neural networks to the problem of alpha matte extraction.

关键词： Alpha matting Digital compositing Green screen Machine learning back-propagation algorithm

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Deterministic and probabilistic occupancy detection with a novel heuristic optimization and back-propagation (BP) based algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期42卷 779-791页

作者： Fatema, Nuzhat Farkoush, Saeid Gholami Hasan, Mashhood Malik, H. Univ Sultan Zainal Abidin UniSZA Fac Business & Management Terengganu Malaysia Intelligent Prognost Private Ltd Delhi India Yeungnam Univ Elect Engn Dept Gyongsan Gyeongsangbuk D South Korea Jazan Univ Coll Appl Ind Technol Elect Power Engn Technol Jazan Saudi Arabia BEARS NUS Campus Singapore Singapore

In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and back-propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA.

关键词： Gravitational search algorithm back-propagation algorithm neural network machine learning optimization occupancy smart building

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An algorithm for learning principal curves with principal component analysis and back-propagation network

An algorithm for learning principal curves with principal co...

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7th International Conference on Intelligent Systems Design and Applications (ISDA 2007)

作者： Wang, Y. H. Guo, Y. Fu, Y. C. Shen, Z. Y. Soochow Univ Suzhou Peoples R China Hohai Univ Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769529769

A new algorithm for learning principal curves with definite mathematical representations is proposed based on combining the principal component analysis (PCA) and back-propagation (BP) network. The algorithm successfully turns an unsupervised learning problem into a supervised one by projecting a data set to its first component line and identifying the relation between the data points and their corresponding projection indices with BP network. This algorithm has been proved distinctly superior to the HS algorithm.

关键词： principal component analysis back-propagation algorithm principal curves

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Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study

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ENVIRONMENTAL MODELLING & SOFTWARE 1998年第2期13卷 179-191页

作者： Maier, HR Dandy, GC Univ Adelaide Dept Civil & Environm Engn Adelaide SA 5005 Australia

In recent years, back-propagation neural networks have become a popular tool for modelling environmental systems. However, as a result of the relative newness of the technique to this field, users appear to have limited knowledge about how ANNs operate and how to optimise their performance. In this paper, the stages observed when training a back-propagation neural network: are examined in detail for a particular case study;the forecasting of salinity in the River Murray at Murray Bridge, South Australia, 14 days in advance. Particular attention is paid to the behaviour of the network as it approaches a local minimum in the error surface. The effect of the presence of infrequent patterns in the training set on generalisation ability is investigated. The nature of the error surface in the vicinity of local minima is examined and options for optimising network performance (i.e. training speed and generalisation ability) are presented for real time forecasting situations. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： Artificial Neural Networks back-propagation algorithm generalisation ability local minima learning speed optimisation stages of learning

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back-propagation-assisted inverse design of structured light fields for given profiles of optical force

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NANOPHOTONICS 2023年第11期12卷 2019-2027页

作者： Zhao, Xiaoshu Lin, Haoze Chen, Huajin Zheng, Hongxia Ng, Jack Fudan Univ State Key Lab Surface Phys Shanghai 200433 Peoples R China Fudan Univ Dept Phys Shanghai 200433 Peoples R China Columbia Univ Dept Comp Sci New York NY 10027 USA Guangxi Univ Sci & Technol Sch Elect Engn Liuzhou 545006 Guangxi Peoples R China Guangxi Coll & Univ Key Lab Microwave Commun & Mi Liuzhou 545006 Guangxi Peoples R China Southern Univ Sci & Technol Dept Phys Shenzhen 518055 Guangdong Peoples R China

Designing a monochromatic spatially-structured light field that recovers the pre-specified profile of optical force (OF) exerted on a particle is an inverse problem. It usually requires high dimensional optimization and involves lengthy calculations, thus remaining little studied despite decades of research on OF. We report here the first attempt to attack this inverse design problem. The modus operandi relies on the back-propagation algorithm, which is facilitated by the currently available machine learning framework, and, in particular, by an exact and efficient expression of OF that shows only polynomial and trigonometric functional dependence on the engineered parameters governing the structured light field. Two illustrative examples are presented in which the inversely designed structured light fields reproduce, respectively, a predefined spatial pattern of OF and a negative longitudinal OF in a transversely trapping area.

关键词： back-propagation algorithm inverse design optical force structured light field

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Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

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ELECTRONICS LETTERS 2003年第5期39卷 447-448页

作者： Ng, SC Cheung, CC Leung, SH Open Univ Sch Sci & Technol Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Comp Engn & Informat Technol Hong Kong Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

An integrated approach of magnified gradient function and weight evolution with deterministic perturbation to improve the performance of back-propagation teaming is proposed. Simulation results show that, in terms of the convergence rate and the percentage of global convergence, the integrated approach always outperforms the other traditional methods.

关键词： convergence performance improvement convergence rate back-propagation algorithm backpropagation learning magnified gradient function Learning in AI deterministic perturbation backpropagation global convergence weight evolution

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The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study

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ENVIRONMENTAL MODELLING & SOFTWARE 1998年第2期13卷 193-209页

作者： Maier, HR Dandy, GC Univ Adelaide Dept Civil & Environm Engn Adelaide SA 5005 Australia

Artificial neural networks of the back-propagation type are being used increasingly for modelling environmental systems. One of the most difficult, and least understood, tasks in the design of back-propagation networks is the choice of adequate internal network parameters and appropriate network geometries. Although some guidance is available for the choice of these values, they are generally determined using a trial and error approach. This paper describes the effect of geometry and internal parameters on network performance for a particular case study. Although the information obtained from the tests carried out in this research is specific to the problem considered, it provides users of back-propagation networks with a valuable guide on the behaviour of networks under a wide range of operating conditions. The results obtained indicate that learning rate, momentum, the gain of the transfer function, epoch size and network geometry have a significant impact on training speed, but not on generalisation ability. The type of transfer and error function used was found to have a significant impact on learning speed as well as generalisation ability. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： artificial neural networks back-propagation algorithm generalisation ability geometry internal parameters learning speed network design

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