evolutionary neural network algorithms
Diagnosis of Breast Cancer by Combining the Techniques of Data Mining and Artificial Immune System
... mining algorithms on the data obtained from the sampling, a higher accuracy can be ...and evolutionary immune system algorithm) to improve ...of neural network weights is done using an ...
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Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
... the algorithms or ...multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural ...multiobjective evolutionary algorithms, which is a ...
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Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
... As a global network, internet refers to computers that work much like the postal system at sub-second speeds. Just as if the postal service allows people to exchange envelopes containing messages among themselves, ...
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Multi-objective evolutionary algorithms of spiking neural networks
... between accuracy and the network structure. Several integrations between EAs and Swarm Intelligence (SI) strategies with ESNN have been performed such as: (Hamed et al., 2009a; Schliebs et al., 2009b; Schliebs et ...
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Time Series Forecasting using Evolutionary Neural Network
... Recurrent neural network [3-5], Radial basis function (RBF) networks [6], Bayesian neural networks [7], and neuron-fuzzy networks [8-9], generalized regression neural network (GRNN) ...
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Evolutionary Algorithms For Neural Networks Binary And Real Data Classification
... Multi-layer Perceptron Networks (MLP) are the most popular feed-forward supervised ANNs. They essentially, consist of a single input, output layer, and one or more hidden layers. One hidden layer is usually sufficient to ...
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Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm
... Artificial neural networks mainly arises from their potential ability to perform interesting computational tasks, it had to adopt themselves under unknown situations by the means their training ...The ...
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COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK
... some evolutionary algorithms (GA, GSA, ES, FES and nine variants of DE) were used and their performance based on Mean-Best-Fitness, Mean-Fitness-Evaluation and STDEV-Best-Fitness factors were compared using ...
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Applying Linear Regression and Neural Network Meta-Models for Evolutionary Algorithm Based Simulation Optimization
... Genetic Algorithms(GA) are a part of a field of computational optimization methods that do not rely on gradients to perform the search and are independent from the evaluation function in making search ...These ...
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An evolutionary algorithm based Feature extraction and selection to Persian and Arabic Handwritten Recognition
... combining neural network with genetic algorithm and quantum genetic algorithm is used to select an effective subset of ...and neural network learning algorithms are heuristic ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... of neural networks in literature re- search by Aston ...“the network can be taught to recognize individual writing styles, and the researchers used it to compare works attributed to Shakespeare and his ...
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Hybrid soft computing systems for electromyographic signals analysis: a review
... (RBF) neural network under some constraints, allowing the system to learn from the training ...(BP) network, conic section function NN, and fuzzy clustering neural network ...
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Evolutionary Load Forecasting using Artificial Neural Network
... Steep raise of power system networks and upturn in their complexity, different features are influential in electric power generation, demand or load management. Load forecasting in the precarious feature for profitable ...
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Optimal Location of STATCOM on Transmission Network using Evolutionary Algorithms
... However the papers approach solely doesn‟t rely on evolutionary techniques, as for the purpose of transmission loss computations at various steps of algorithm Load flow analysis has to be done. Load flow ...
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Evaluation Of Different Software Based Approaches For Deep Packet Inspection
... Artificial Neural Network (ANN) is very useful for pattern matching ...artificial neural nets have been applied extensively is optical character recognition ...
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Validation of Image Compression Algorithms using Neural Network
... The above sample shows that the Neural network was trained using training set which consist of 30 images. It was then tested for set of set of testing images the result is depicted in Table 6. The desired ...
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A HYBRID MODEL OF PARTICLE SWARM AND ANT COLONY OPTIMIZATION ALGORITHM FOR TEST CASE OPTIMIZATION
... Luciano S. de Souza proposed a multi-objective Particle Swarm Optimization (PSO) for selecting the test cases based on functional requirements coverage and execution effort. Binary Multi-Objective PSO (BMOPSO) and Binary ...
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Detecting Network Intrusion Using BPA & RBF Neural Network Algorithms
... A connection in the KDD-99 dataset is represented by 41 features. The features in columns2, 3, and 4 in the KDD99 dataset are the protocol type, the service type, and the flag, respectively. Thevalue of the protocol type ...
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The Application of Evolutionary Algorithms for Energy Efficient Grooming of Scheduled Sub-Wavelength Traffic Demands in Optical Networks
... We have shown, through simulations, that both approaches can be used to handle larger networks with many demands – situations in which ILPs would become computationally intractable and not practical – and lead to ...
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A Comparative Study on Various Neural Network Algorithms
... Artificial Neural Networks (ANN) is a mathematical or computational model based on biological ...terms, neural networks are nonlinear statistical data modelling ...
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