Training Feed-forward neural networks
A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-learning Context
... IV. Conclusions A comparison of algorithms for training feed forward neural networks was done. Tests were done on two gradient descent algorithms: Backpropagation and ...
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
... (2005). Training feed-forward neural networks with ant colony optimization: An application to pattern ...(2013). Training feed-forward neural networks ...
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An Evolutionary Approach to Training Feed-Forward and Recurrent Neural Networks.
... The gene structure used by Krishnan is an attempt to overcome the problems associated with encoding real numbers onto a chromosome represented as a bit string. In the 2DELTA-GANN method, each gene on the chromosome is a ...
Topographic mappings and feed-forward neural networks
... CALE networks exhibited lower curvature and test S TRESS than these a posteriori mod- els in all ...the training error, but rather to minimise training error while at the same time seeking a rotation ...
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Classification of Arecanut using Neural Networks with Feed forward Techniques
... using feed forward NN ...for training 144 images, validation 36 images and testing 60 images and four class target value shows in ...of neural network, ...
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Optimizing the Multilayer Feed Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm
... the neural network to avoid inadequate current trial-and-error approach and to determine a set of initial process parameters for injection moulding ...moulding, neural network (NN) was developed and ...
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Foreground-Background Separation by Feed-forward Neural Networks in Old Manuscripts
... Then, the classification is done differently in the three approaches. In the first approach, the neurons of the SOM are segmented into N regions (for N classes) using the Kmeans algorithm [9]. Once the pixels are ...
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Challenges to Multi-Layer Feed Forward Neural Networks in Intrusion Detection
... multi-layer feed forward neural network architecture is a commonly used model for working with anomaly-based intrusion detection systems (Bhaskar, Kamath & Moitra, 2008; Wei, Hao-yu, Xu, Yu-xin ...
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Generalization properties of feed-forward neural networks trained on Lorenz systems
... Abstract. Neural networks are able to approximate chaotic dynamical systems when provided with training data that cover all relevant regions of the system’s phase ...of feed-forward ...
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Partial Learning Machine: A New Learning Scheme for Feed Forward Neural Networks
... longest training time and the average training time is about ...the training time of the BP algorithm on this dataset is still the ...the training time of BP is the ...the training ...
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Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks
... single-layered feed-forward neural network architecture, comprised of six input neurons and one output neuron, was designed for model training and development, as shown in Figure ...into ...
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A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions
... two neural network ap- proaches to accurately discriminate between three tissue types: significant pros- tate cancer lesions, non-significant lesions, and healthy ...interest. Feed-Forward and ...
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A Novel Strategy for Weight Initialization in Sigmoidal Feed-forward Artificial Neural Networks
... Abstract—Among the various parameters of a neural network, the initial values of the weights and biases pertaining to each artificial neuron have been given a lot of importance. In this paper, a novel method of ...
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Cooperative coevolution of feed forward neural networks for financial time series problem
... of Feed Forward Neural Networks for Financial Time Series Problem Shelvin Chand and Rohitash Chandra Abstract— Intelligent financial prediction systems guide in- vestors in making good ...
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ParkinsoNET: Estimation of UPDRS Score using Hubness-aware Feed-Forward Neural Networks
... The data recorded during spontaneous usage of smartphones and tablets was originally not designed for diagnostic purposes, therefore, even the most useful pieces of the data can only be expected to be weakly correlated ...
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Part of Speech Driven Cross Lingual Pronoun Prediction with Feed Forward Neural Networks
... separate training data collections were available, we only found interesting results when using data from the same domain as the test data, ...of training data such as Europarl or news text, ...
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Leukemia Diagnosis using Back Propagation Multi-layer Feed Forward Neural Networks
... ABSTRACT: The term “Leukemia” refers to cancer of the blood cells. The presence of Eosinophil, Neutrophil, Lymphocyte, Basophil, and Monocyte in the blood test and the bone marrow indicates that the cells are influenced ...
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Assamese Digit Recognition with Feed Forward Neural Network
... as neural networks classifier, tree based classifier, Hidden Markova Model (HMM) based classifier ...Artificial neural networks can handle non-convex decisions. Multi-layer feed ...
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Large tagset labeling using Feed Forward Neural Networks Case study on Romanian Language
... speed, training speed, hidden layer configuration and the number of optimal training ...of training iterations, the training and the runtime speed are all highly dependent on the hidden layer ...
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Efficient Cancer Clustering and Classification Using Multi Layer Feed Forward Artificial Neural Networks
... Training the MFFANN the input photo is cut up into blocks or vectors of 4x4, 8×8 or 16×16 pixels. Those vectors are used as inputs to the community. The network is offer by the predicted (or the favored) output, ...
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