optimal neural network weights
Simultaneous Evolution of Architecture and Connection Weights in Artificial Neural Network
... small network, and hidden nodes, layers and connections are added to the network dynamically [2], but in the destructive algorithm starts with large network and hidden nodes and connections are ...
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Studies on Optimization Algorithms for Some Artificial Neural Networks Based on Genetic Algorithm (GA)
... connection weights of the neural network may be more efficient than the traditional gradient descent based learning algorithms, but it still should try the network architecture, in this paper, ...
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Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction
... higher network complexity which suggests the need for Deep Neural ...the network has to be split into three sets; training set, validation set and the testing ...the network is achieved by ...
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Evolving binary-weights neural network using hybrid optimization algorithm for color space conversion
... Constructing neural networks involves dicult optimization problems, such as nding a net- work architecture appropriate for the application at hand, and nding an optimal set of weight values for the ...
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A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing
... initialized weights of the network will be updated based on (6). The network uses only one set of initial weights and based on the error rate at the output stage, and the learning rate the new ...
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Performance Analysis On Metaheuristic Based Hybrid Neural Network To Predict The Stock Movement
... based neural network learning scheme has been developed that alleviates the existing Artificial Neural Network (ANN) limitations such as local minima and convergence ...Hybrid Neural ...
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THE OPTIMAL PERFORMANCE OF MULTI-LAYER NEURAL NETWORK FOR SPEAKER-INDEPENDENT ISOLATED SPOKEN MALAY PARLIAMENTARY SPEECH
... (MLP) Neural Networks with two-layer feedforward network configurations that are trained with stochastic error back-propagation to adjust its weights and biases after presentation of every training ...
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A New Approach to Persian and Arabic Handwritten Character Recognition with Hybrid of Artificial Neural Network and Genetic Algorithm
... artificial neural networks is to find the optimal values for different layers of neural network weights and biases [12] and network needs to find optimal values for them, ...
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The use of adversaries for optimal neural network training
... The input data is pre-processed by NB to transform each variable into a Gaussian dis- tribution. The batch-size was 100, and NB was trained over 150 epochs using the Broy- den–Fletcher–Goldfarb–Shanno algorithm (see [12] ...
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APPLICATION OF CELLULAR AUTOMATA FOR MODELING AND REVIEW OF METHODS OF MOVEMENT OF A GROUP OF PEOPLE
... get optimal weights for backpropagation network, then they used back propagation neural network, after that they trained and tested the network using optimal ...
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Online Full Text
... multi-layer network of Perceptron by simply offering explanation in relation to weights and suitable ...of network an input layer for applying input of problem, a hidden layer and an output layer ...
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Optimal design of R.C beams using neural network
... finalized Minimizing Beam Element Total Cost (BETC). Material and labor and formwork costs are also found out. This paper deals with designing a low cost of RCC beam in MATLAB. The results from the software and the ...
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Model of Electric Power Load by Adaptive Neural Network
... of network structure demonstrate that training error steadily decrease with an adaptive learning factor starting at different initial values whereas errors behave volatile with constant learning ...
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A Quantitative Structure-activity Relationships
... (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the 15.. weights of back propagation artificial neural network (BP ANN).[r] ...
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Answering questions by learning to rank Learning to rank by answering questions
... Last but not least, we deployed a pre-trained transformer, BERT (Devlin et al., 2018), both the base version with 12 layers and the large version with 24 layers of transformers. BERT is currently the state-of-the-art ...
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The Backpropagation Algorithm
... the neural network community needed had already been developed by researchers working in the field of optimal ...of neural networks, a function f must be found and a set of input-output values ...
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Optimal inflation weights in the Euro Area
... The frequency of price changes, calculated within the Euro System Inflation Persistence Network as an average over the period 1996-2001, represents the average share of prices that are revised in a given month ...
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Domain Adaptation of SRL Systems for Biological Processes
... CNN-LSTM-CRF: The CNN-LSTM-CRF model on ProcessBank achieves 40.62 F1 without any pre-training. This result is comparable to the baseline, showing the importance of ini- tialization of weights while training a ...
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Generating and Exploiting Large scale Pseudo Training Data for Zero Pronoun Resolution
... comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to over- come the gap between the pseudo training data and the real ...
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Research on Classification of E-shopper Based on Neural Networks and Genetic Algorithm
... and neural network etc are usually used to mine e-shopper’s transaction database in order to classify ...artificial neural network is the important one of the ...fact, neural ...
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