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5th International Conference on Machine Learning and Cybernetics

作者： Wang, Yong Liu, Ji-Zhen Liu, Xiang-Jie North Elect Power Dept Automat Beijing 102206 Peoples R China Chang Chun Inst Technol Dept Comp Changchun 130012 Peoples R China

ISBN: (纸本)1424400600

Super-heater steam temperature in power plant is the strong nonlinearity system. Though neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the "best" structure of the neural network is more difficulty. Sparse Least squares support vector networks (SLSVN) are proposed to model the superheated steam of power plant in this paper. The structure of the SLSVN is obtained by equality-constrained minimization. Under the condition of modeling approximating to performance, the pruning algorithm gets the sparse modeling. The merits-of the algorithm are conforming to the least structural risk in training process and hardly leading to over-fitting. The simulation of a superheating system, in a 600MW supercritical concurrent boiler, is taken. The result shows that the proposed SLSVN model can adapt to the strong nonlinear super-heater steam temperature process.

关键词： support vector networks RBF pruning algorithm sparseness least squares

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Research on Trim of Multilayer Feedforward Small World Network Based on E-exponential Information Entropy 9

Research on Trim of Multilayer Feedforward Small World Netwo...

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9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Guo, Dongwei Liu, Yang Jilin Univ JLU Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781538630228

In recent years, it has been recognized that the there is a gapbetween artificial neural network and the brain neural network. The biological neural network is neither a random network nor a regular network, it is a network structure between the two. While the small world network has both a larger clustering coefficient of a regular network and a smaller average path length of a random network [1], so the superiority of the small world network has aroused people's attention. As the BP algorithm converges slowly in the process of error back propagation, it is easy to fall into the local minimum point in the modified weight stage, so this paper Optimizes the BP algorithm to improve the convergence rate of the network and improve the problem that the network is easy to fall into the local minimal. The connection between nodes is too close because there are too many hidden nodes, as a result, the problem of overfitting is arisen. In other words, as for data that are not in the training sample, the learning ability of the network is not strong, resulting in a decrease in the practical value of the network. So we need to find a suitable network structure [2]. Therefore, this paper proposes a e-exponential information entropy multilayer feedforward small world network pruning algorithm based on Shannon entropy principle. By constantly training the network, the network is trimmed according to the entropy of the hidden nodes until the network tends to be stable. The experimental results show that the it has been improved obviously in terms of calculated error and test accuracy for trimmed network, which improved the problem of over-fitting to some extent.

关键词： small world network multilayer feedforward network overfitting pruning algorithm

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The Computer Interlocking Software System Reliability Test Based on the Monte Carlo

The Computer Interlocking Software System Reliability Test B...

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International Conference on Machinery, Electronics and Control Simulation (ICMECS)

作者： Guan, Qiong Tao, Han Qing Huang, Bin Liuzhou Railway Vocat Tech Coll Liuzhou Peoples R China

ISBN: (纸本)9783038352105

The railway switch failure prediction for railway signal equipment maintenance plays an important role. The paper put forward railway switch failure prediction algorithm based on least squares support vector machine, and chose five characteristic indexes composed of railway switch failure prediction models characteristic input vectors. It reduces the dimension of input vectors, shorten the least squares support vector machine training time, and use a pruning algorithm to accelerate the computing speed maintaining a good regression performance at the same time. The experiment proved that railway switch failure prediction algorithm has strong self-learning ability and higher prediction accuracy based on least squares support vector machine. And it can accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction.

关键词： Railway switch Failure Prediction Least Squares Support Vector Machine Characteristic Index pruning algorithm

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Estimation of myriad haplotype frequencies

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Genetic Epidemiology 1985年第3期2卷 263-272页

作者： Maclean, Charles J. Morton, Newton E. Population Genetics Laboratory University of Hawaii Honolulu United States

The present paper demonstrates algorithms for applying gene counting estimation of haplotype frequencies in very large genetic systems. A factor union representation of phenotypes is used which conveniently yields the sets of potential haplotypes and diplotypes for each phenotype. Methods for storing and rapidly retrieving the relevant haplotypes are given. An example with several hundred frequencies is given which required a few seconds computing time for each estimation iteration on a small computer. A computer program employing the methods described has been written in Fortran-77 and is available to any investigator on request. Copyright © 1985 Wiley-Liss, Inc., A Wiley Company

关键词： factor union notation gene counting haplotype estimation pruning algorithm

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An Efficient Adaptive Fuzzy Neural Network (EAFNN) Approach for Short Term Load Forecasting

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10th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2010)

作者： Du, Juan Er, Meng Joo Rutkowski, Leszek Nanyang Technol Univ Sch EEE Singapore 639798 Singapore Czestochowa Tech Univ Dept Comp Engn PL-42200 Czestochowa Poland Acad Management SWSPiZ Inst Informat Technol PL-90113 Lodz Poland

ISBN: (纸本)9783642132070

In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. It can not only reduce the complexity of the network but also accelerate the learning speed. The proposed EAFNN method is tested on the actual electrical load data from well-known EUNITE competition data. Results show the proposed approach provides the superior forecasting accuracy when applying in the real data.

关键词： Fuzzy Neural Network Short Term load forecasting pruning algorithm

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Real-time Vehicle Detection and Tracking Based on YOLOv3 pruning Model

Real-time Vehicle Detection and Tracking Based on YOLOv3 Pru...

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第33届中国控制与决策会议

作者： Mingxiu Lin Jiayi Li Jiaxin Zhang Xinghui Li Shiyao Ji Yunli He College of Information Science and Engineering Northeastern University

Real-time detection and tracking of vehicles is very important in the field of automatic *** this paper,a YOLOv3 network based on pruning algorithm is proposed to solve the problem of real-time vehicle *** reducing the number of channels and layers in the backbone network,the computation of the model is *** thus,the rate of detection is greatly *** the basis of vehicle detection,the real-time tracking of multiple vehicle targets is completed by using Kalman filter algorithm for prediction and Hungarian algorithm for data *** experimental results show that compared with the original YOLOv3 network,the model size is compressed by 95% to11.25 MB and the detection rate is doubled to 128.1 frames/s while the average accuracy is basically *** detection and tracking frame rate of the whole algorithm is 18 fps,and the recall is 98.5%.The algorithm also has strong robustness on complex traffic roads,and can basically realize real-time detection and tracking of road vehicles.

关键词： Real-time detection and tracking pruning algorithm Kalman filter algorithm Hungarian algorithm

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An Efficient CNN Training Accelerator Leveraging Transposable Block Sparsity 4

An Efficient CNN Training Accelerator Leveraging Transposabl...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era

作者： Xu, Mingyang Lu, Jinming Wang, Zhongfeng Lin, Jun Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (数字)9781665409964

ISBN: (纸本)9781665409964

Convolutional neural network (CNN) training is computationally intensive, requiring a great deal of time and resources. Exploiting data sparsity is a promising method to accelerate CNN training. In this work, we propose a novel algorithm for sparse training processes in which the weight matrices are pruned in a fine-grained block-wise manner. Both the forward propagation (FP) and backward propagation (BP) phases use the identical data layout. It can eliminate the matrix transposition procedure, reducing storage space and training time. Based on this pruning approach, we developed an FPGA-based accelerator for CNN training using a systolic array. The architecture can effectively skip the zero values calculation without incurring the imbalance between different processing elements (PEs). Our experimental results indicate that our design achieves 1.024 TOPS and 118.4 GOPS/VV in terms of computational throughput and energy efficiency. Our design is 1.41x similar to 4.93x more energy efficient than the state-of-the-art training accelerator.

关键词： CNN sparsity pruning algorithm hardware accelerator

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PyEvolve: a toolkit for statistical modelling of molecular evolution

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BMC BIOINFORMATICS 2004年第1期5卷 1-1页

作者： Butterfield, A Vedagiri, V Lang, E Lawrence, C Wakefield, MJ Isaev, A Huttley, GA Australian Natl Univ John Curtin Sch Med Res Ctr Bioinformat Sci Canberra ACT 0200 Australia Australian Natl Univ Inst Math Sci Canberra ACT 0200 Australia HeliXense Pte Ltd Singapore 118254 Singapore Australian Natl Univ Res Sch Biol Sci ARC Ctr Kangaroo Genom Canberra ACT 0200 Australia

Background: Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences - ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. Results: Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from similar to10 days to similar to6 hours. Conclusion: PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The

关键词： Partial Likelihood pruning algorithm Subgroup Size Codon Model Real World Performance

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Fuzzy Neural Network for Pattern Classification

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Procedia Computer Science 2020年 167卷 2606-2616页

作者： Arun Kulkarni Nikita kulkarni Research Scholar SGGS Institute of Engineering and Technology Nanded-413601 India Associate Applications developer Oracle Financial Services software Ltd. Goregaon(E) Mumbai 400063 India

This research work proposes a fuzzy neural network (FNN) for pattern classification. The proposed network is the modified version of the Radial basis function neural network (RBFNN). FNN uses supervised fuzzy clustering and pruning algorithm to determine the precise number of clusters with proper centroid and width to form the processing nodes in the hidden layer. These clusters represent fuzzy set hyperspheres (FSHs), which are defined by the fuzzy membership function. The training between the hidden layer to output layer which is done by using the LMS algorithm in RBFNN is avoided, and the output is determined by using the fuzzy union operation. The fuzzy membership function shields the clustered patterns resulting in 100% accuracy for the data set used during training. Unlike other clustering algorithms used to construct the hidden layer of RBFNN, the proposed clustering algorithm is independent of tuning parameters and is fast in training and retrieval. Thus FNN reduces the computation time, guarantees 100% accuracy for any training set, and provides superior and comparable recognition accuracy for the datasets with the precise number of FSHs in the hidden layer. Hence the proposed FNN can be used for pattern classification.

关键词： Fuzzy neural network Radial basis function neural network pruning algorithm Fuzzy Membership function

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An Improved Adaptive Thinning Framework

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ENGINEERING LETTERS 2022年第3期30卷 1138页

作者： Ma, Jun Ren, Xunhuan Liu, Yuan Tsviatkou, Viktar Yurevich Kanapelka, Valery Kanstantinavich Belarusian State Univ Informat & Elect Minsk BELARUS Belarusian State Univ Informat & Elect Dept Infocommun Technol Minsk BELARUS

The thinning algorithm is one of the fastest approaches to extract skeletons from an object, especially when adopting the parallel strategy. Skeletons are very useful descriptors and can be applied in many recognition fields. However, one of the drawbacks that limits the use of these techniques is that thinning algorithms are not robust against inner noise and outer noise, which may produce many unwanted branches. To alleviate the influence of noise and increase the robustness, pruning methods and scale-space methods have been proposed in the past, in which pruning methods are aimed at suppressing the outer noise (boundary noise) and scale-space methods are aimed at suppressing the inner noise (such as scratch noise and dithering noise). In this paper, we proposed an improved framework that can deal with both inner noise and outer noise. The experiment proved that the proposed framework has better visual effects than the existing pruning method and existing scale-space method. In addition, the proposed framework is an adaptive framework that does not require manual tuning of parameters.

关键词： Adaptive framework Thinning algorithm Robustness against noise pruning algorithm scale-space filtering

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