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A Study on Single and Multi-layer Perceptron Neural Network 3

A Study on Single and Multi-layer Perceptron Neural Network

引用

3rd International Conference on Computing Methodologies and Communication (ICCMC)

作者： Singh, Jaswinder Banerjee, Rajdeep KIIT Univ Sch Comp Engn Bhubaneswar India KIIT Univ Sch Mech Engn Bhubaneswar India

ISBN: (纸本)9781538678084

Perceptron is the most basic model among the various artificial neural nets, has historically impacted and initiated the research in the field of artificial nets, with intrinsic learning algorithm and classification property. It has boosted the world of neural networks and profoundly impacted the numerous advancements. From the very beginning it has proved to be the key to the way machines perceive, making them artificially intelligent through extensive training processes. In this study, the ideology of perceptron learning, its concepts, working, applications and a very brief introduction to multi - layer perceptron has been discussed.

关键词： Neural network Feed Forward Network artificial neuron activation functions training algorithm neuron structure prediction multi layer neuron

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The neural network model based on PSO for short-term load forecasting

The neural network model based on PSO for short-term load fo...

引用

5th International Conference on Machine Learning and Cybernetics

作者： Sun, Wei Zhang, Ying-Xia Li, Fang-Tao North China Elect Power Univ Dept Econ & Management Baoding 071003 Peoples R China Jiangsu Huaneng Huaiyin Power Plan Dept Engn Huaiyin 223001 Peoples R China

ISBN: (纸本)1424400600

A new algorithm for load forecasting--the neural network model based on Particle Swarm Optimization (PSO-NN) for short-term load forecasting is proposed in this paper. The method is simple, easy to realize and its convergence rate is quick. The overall optimal solution of the problem can be found in great probability, and the intrinsic defects of artificial neural network, such as slow training speed and the existence of local minimum points, can be effectively overcome. Simulation results show that forecasting precision and speed can be improved by this method, and its forecasting capability is obviously better than the neural network model based on BP algorithm (BP-NN).

关键词： load forecasting particle swarm optimization neural network training algorithm

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On Optimal Control Scheme Based on Feedforward and Inverse Models of Artificial Neural Network

On Optimal Control Scheme Based on Feedforward and Inverse M...

引用

International Conference on Computer Science and Education (CSE 2011)

作者： Qu, Dongcai Zhao, Guorong Cao, Dong Lu, Jianhua Lu, Binwen Dept Control Engn Naval Aeronaut Yantai 264001 Peoples R China Naval Fly Acad PT-125000 Huludao Peoples R China

ISBN: (纸本)9783642224171

The mostly optimum control questions were based on precise mathematical model of the controlled object at present, and only aimed at the few parameters implementation optimization control. Because it was very difficult or imposable that established precise mathematical model of actual controlled *** actual massive complex. unknown and indefinite nonlinear objects had many optimization parameters, it made conventional optimum control method no longer to be suitable to. In order to solve these problems, in the analysis optimum control principle foundation, the optimum control scheme based on the ANN's (Artificial Neural Network) feedforward / inverse model had designed. Basis on characteristics of nonlinear mapping and adaptive study of ANN's models and so on, the configuration of the ANN's feedforward and inverse models were designed. Used specialized training method of ANN models, and based on the EF(Exponential Forgetting) algorithm renewed covariance matrix, iterative training online to ANN's models were carried out, therefore obtained the ANN's models with the optimization configuration. Simulation results show that, designed optimum control structure scheme is reasonable, and ANN's controller may get good controlling effect.

关键词： Optimum control Neural Network(NN) training algorithm

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A soft sensing method based on process neural network

A soft sensing method based on process neural network

引用

IEEE International Conference on Information Acquisition

作者： Liu Zaiwen Lian Xiaoqin Wang Zhenaxiang Wang Xiaoyi Hou Chaozhen Beijing Technol & Business Univ Sch Informat Engn Beijing 100037 Peoples R China Beijing Inst Technol Sch Informat Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)1424405289

A new method of soft sensing based on process neural network (PNN) is proposed in this paper. PNN is an extent of traditional neural network, and it is a new configuration of artificial neural network put forward in recent years. The thesis discuss some modified algorithms for raising training speed of PNN, these algorithms are based on function orthogonal basis expansion which exist low-speed convergence in network training. An improved algorithm for BP network based on function orthogonal basis expansion in, process neural network for soft sensing is researched. After increasing the normalizing rule on original algorithm, and introducing function momentum adjustment item and learning rate automatically adjustment method for network weight function, which has means of zero and standard deviations of one, the training time of learning algorithm for process neural network is reduced, and a good effect is represented by simulation in wastewater treatment system.

关键词： soft sensing process neural network training algorithm wastewater treatment simulation

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Wavelet Neural Network Applied to Power Disturbance Signal in Distributed Power System

Wavelet Neural Network Applied to Power Disturbance Signal i...

引用

21st Chinese Control and Decision Conference

作者： Huang Weili Du Wei Hebei Univ Engn Handan 056038 Peoples R China

ISBN: (纸本)9781424427239

The power system load equipment is more sensitive to power quality disturbances than equipment applied in the past. Therefore, the electric supply quality has become a major concern of electric utilities and end-users. A novel approach to detect and locate power quality disturbance in distributed power system combining wavelet transform with neural network is proposed. By performing decomposition of transient waveform, the original signal is divided into two parts: the low-frequency and the high-frequency, corresponding to approximation part and details part respectively. The paper aims at complex wavelet analysis, and then explores feature extraction of disturbance signal to obtain dynamic parameters, superior to real wavelet analysis result. The characteristic vector obtained from wavelet decomposition coefficients are input data of neural network for power quality disturbance pattern recognition. The improved training algorithm is used to complete the network parameter identification. By means of simulation and experimental data, the disturbance pattern can be obtained from the neural network output. The simulation results show that the proposed method is effective for transient signal analysis, taking advantage of complex wavelet transform and neural network.

关键词： Power quality disturbance detect and locate signal decomposition neural network training algorithm characteristic vector

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A SYNTHETIC NEURAL INTEGRATED-CIRCUIT

A SYNTHETIC NEURAL INTEGRATED-CIRCUIT

引用

1989 IEEE CONF ON CUSTOM INTEGRATED CIRCUITS ( CICC 89 )

作者： AKERS, LA WALKER, M GRONDIN, R FERRY, D Center for Solid State Electronics Research Arizona State University Tempe AZ 85287-6206 United States

Integrated circuits are approaching biological complexity in device count. Biological systems are fault tolerant, adaptive, and trainable, and the possibility exists for similar characteristics in ICs. The authors report a limited-interconnect, highly layered synthetic neural network that implements these ideals. These networks are specifically designed to scale to tens of thousands of processing elements on current production size dies. A compact analog cell, a training algorithm, and a limited-interconnect architecture which has demonstrated neuromorphic behavior are described.

关键词： synthetic neural integrated circuit biological complexity device count highly layered synthetic neural network tens of thousands of processing elements current production size dies compact analog cell training algorithm limited-interconnect architecture neuromorphic behavior neural nets VLSI

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Optimistic bias in the assessment of high dimensional classifiers with a limited dataset

Optimistic bias in the assessment of high dimensional classi...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Chen, Weijie Brown, David G. US FDA Silver Spring MD 20993 USA

ISBN: (纸本)9781424496365

It is commonly recognized that using the same dataset for training and testing the classifier introduces optimistic bias in estimating classifier performance. However, bias of the same kind may still exist even when independent datasets are used for training and testing a classifier. This problem is especially important in the setting of high dimensional feature space and limited data. Bioinformatics data is typically characterized by a tremendous amount of data per patient but from a limited number of patients. Often the entire data set is utilized in a "pre-training" stage during which the feature set is winnowed to a manageable number, and the parameters of the training algorithm are established. Subsequently the data is bifurcated into training and test sets;however, bias has already been introduced into the classifier development process. We investigate the significance of this bias by performing simulated gene expression experiments. We find that, for data with moderate intrinsic separability and modest sample size, any observed separation is due to selection bias introduced in the aforementioned pre-training process. For greater intrinsic separability, correct data hygiene, i.e., complete separation of development and validation data yields a positive result, but one far less impressive than that mistakenly obtained using incomplete data separation.

关键词： Breast cancer Classification algorithms Covariance matrix Measurement Signal to noise ratio Testing training bioinformatics bioinformatics data classifier development process gene expression genetics high dimensional classifier optimistic bias pattern classification training algorithm

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A training method for SpikeProp without redundant spikes - Removing unnecessary sub-connections during training

A training method for SpikeProp without redundant spikes - R...

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International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Takutoshi Nakayama Takashi Matsumoto Haruhiko Takase Hiroharu Kawanaka Shinji Tsuruoka Graduate School of Engineering Mie university Tsu Mie 514-8507 Mie university Tsu Mie 514-8507

ISBN: (纸本)9781509040940

SpikeProp, which is proposed by Bohte and extended by Booij, is a type of multi-layer networks of spiking neurons. Our research group has proposed a training algorithm for SpikeProp without redundant output spikes. However, its performance is sensitive to the initial network structure, such as the number of hidden units, the number of sub-connections, and their delays. In this article, we discuss the problem, especially the number of sub-connections. And we propose a method to remove unnecessary sub-connections during training. By simple experiments, we show that the method brings less dependency of training performance on the number of initial sub-connections.

关键词： Spiking Neural Network SpikeProp training algorithm Time Series Signal Processing

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Designing Stepped Impedance Microstrip Low-Pass Filters Using Artificial Neural Network at 1.8 GHz

Designing Stepped Impedance Microstrip Low-Pass Filters Usin...

引用

3rd International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kushwah, Vivek Singh Tomar, Geetam S. Bhadauria, Sarita Singh Amity Sch Engn & Technol Dept Elect Gwalior India

ISBN: (纸本)9780769549583;9781467356039

In this paper a design technique for a Stepped impedance Microstrip Low-pass filters is presented by using the artificial neural network (ANN) modeling method. Required dimensions of the microstrip filter layout are used to get the input-output relationships in the ANN model. This paper presents the design and analysis of Stepped impedance Microstrip Low-pass filters at cut-off frequency 1.8 GHz which gives insertion loss (S-21) of -3 dB at cut-off point. Subsequently an artificial neural network model is developed to find out the Magnitude variation of scattering parameters (S-parameters) of Microstrip Low-pass filters at 1.8 GHz for different dimensions. After developing the ANN model of microstrip low-pass filter, it is computationally more efficient in the design and it has been displayed to be as accurate as an Electromagnetic simulator. The simulation is performed using the commercial software IE3D 14.1 and MATLAB programming language GUI Tools are used for ANN training.

关键词： Microstrip Low Pass Filters Lumped elements ANN model MATLAB IE3D EM Simulation S-parameters training algorithm

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Performance of wavelet analysis and neural network for detection and diagnosis of rotating machine fault

Performance of wavelet analysis and neural network for detec...

引用

7th International Symposium on Instrumentation and Control Technology

作者： Kang Shanlin Kang Yuzhe Chen Jingwei Hebei Univ Engn Sch Sci Handan 056038 Peoples R China Beijing Univ Chem Technol Diag & Self Recovery Engn Res Ctr Beijing 100029 Peoples R China

ISBN: (纸本)9780819473622

A novel approach combining wavelet transform with neural network is proposed for vibration fault diagnosis of turbogenerator set in power system. The multi-resolution analysis technology is used to acquire the feature vectors which are applied to train and test the neural network. Feature extraction involves preliminary processing of measurements to obtain suitable parameters which reveal weather an interesting pattern is emerging. The feature extraction technique is needed for preliminary processing of recorded time-series vibrations over a long period of time to obtain suitable parameters. The neural network parameters are determined by means of the recursive orthogonal least squares algorithm. In network training procedure, much simulation and practical samples are utilized to verify and test the network performance. And according to the output result, the fault pattern can be recognized. The actual applications show that the method is effective for detection and diagnosis of rotating machine fault and the experiment result is correct.

关键词： Fault detection vibration fault neural network training algorithm rotating machine

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