feedforward neural networks training
Using artificial neural network to monitor and predict induction motor bearing (IMB) failure
... artificial neural network (ANN) model of induction motor bearing (IMB) failure ...two networks were tested; Feedforward Neural Network (FFNN) and Elman Network for the performance of ...
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TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK
... The Software Defined Networking (SDN) is a advanced and innovative force in the networking industry that change almost every player including network organizers, manufactures, ISPs and cloud service providers. With SDN, ...
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CONSTRUCTIVE TRAINING ALGORITHM FOR DESIGNING FEEDFORWARD NEURAL NETWORKS: A REVIEW Kiran Khatter*, Jaswinder Kaur
... FNN training is to control the complexity and provide good generalization ...in neural network literature to control the complexity of the network ...
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Polytopes as vehicles of informational content in feedforward neural networks
... the training set), but given the right circumstances, we might get close, in which case simple thresholds over the output unit's activation values will be enough to classify input patterns as either belonging to š„ ...
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TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK
... In this study, we adopt the ABC algorithm as a concrete method to enhance the multiple channels in packet radio networks (PRNETs) and wireless mesh networks (WMNs) [5]. PRNET is a type of wireless network ...
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TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK
... artificial neural network models are widely used so that there is a need to understand theory that stands behind ...Artificial neural networks have been find in working areas such as process control, ...
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Using constraints to improve generalisation and training of feedforward neural networks : constraint based decomposition and complex backpropagation
... of training only one neuron at a time and gradually building the net are present in the Cascade Correlation (CC) net proposed by Falhman and Lebiere in [Falhman, ...whole training set is that the solution ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... Zhen Guo che, Tzu-An Chiang and Zhen Hua Che [20] has done one of the comparison between genetic algorithm and back propagation learning algorithm over different problems such as Sin funciton, Iris plant and Diabetes ...
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Adaptive Nonlinear Observer Design Using Feedforward Neural Networks
... Recurrent Neural Networks (DRNN), as observers for nonlinear ...for neural networks ...recurrent neural networks as matrix equation solvers and utilized this scheme to synthesize ...
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A New Optimization Algorithm for Single Hidden Layer Feedforward Neural Networks
... the neural parameters, weights and thresholds, we then deļ¬ne an iterative process, the CCA learning algorithm to obtain the optimal choice of the con- nection ...in training the single hidden layer ...
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On the Use of Deep Feedforward Neural Networks for Automatic Language Identification
... We evaluate all proposed systems in the framework of the NIST LRE 2009 (LREā09) evaluation. The LREā09 in- cludes data from two different audio sources: Conversational Telephone Speech (CTS) and, unlike previous LRE ...
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The accuracy of a procedural approach to specifying feedforward neural networks for forecasting
... sequential training procedure has been used to build a feedforward neural network model ...the training process (as the processing times included in Appendix 1 show) and to help build a ...
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Event Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse
... ward Neural Network with an enriched feature ...The training set was under-sampled randomly: positive examples were pruned in order to obtain the same amount of negative and positive ...
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Feedforward neural networks with constrained weights
... C.6 Decision surfaces after 6 consecutive training runs on problem CI: 2:3:1 network with double-precision weights a-f; 2:3:1 network with integer g-1.. 7 Decision surfaces after 6 conse[r] ...
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Research status and applications of nature-inspired algorithms for agri-food production
... the neural network process models were inversed through numerical optimization to design and implement model predictive controllers to handle the nonlinearity and input-output time lags of the process [46-50] ...
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An Efficient FPGA Implementation of Optical Character Recognition System for License Plate Recognition
... the feedforward neural network, Matlab has been used as the design ...the neural network toolbox integrated into Matlab makes the training of the networks much faster and the design ...
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Neural Networks
... Neural networks, with their exceptional ability to derive definition from imprecise or complicated data, and can be used to separate patterns and detect fashion which is too complex to be observed by either ...
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Capacity of two-layer feedforward neural networks with binary weights
... The hidden and output units have integer and zero thresholds, respectively. We then use a similar approach to that used by Baum to find a lower and an upper bound for the capacity of such networks. The lower bound ...
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Vol 9, No 5 (2019)
... Convolutional Neural Network is widely used concept in recent era to solve the problems like data classification mainly image ...artificial neural network and scene is classified into different ...of ...
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Towards comprehensive foundations of computational intelligence
... hybrid neural-symbolic architectures [176], but not much effort devoted to neural architectures capable of representations of predicate ...activation networks allow for some reasoning (mostly ...
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