Neural network learning
Particle swarm optimization for neural network learning enhancement
... feedforward neural network ...Feedforward Neural Network (PSONN) and Genetic Algorithm Backpropagation Neural Network (GANN) have been ...
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Bacterial foraging optmization algorithm for neural network learning enhancement
... BFOA-based neural network and PSO-based neural network is analyzed; as a result we can decide which method is better for neural network ...
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Neural Network Learning Theoretical Foundations
... It is natural to ask whether there is a Combinatorial' measure analogous to the VC-dimension that can be used to bound the cover- ing numbers of a class of real-valued functions, and hen[r] ...
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Random neural network learning heuristics
... Random Neural Network is a probabilitsic queueing theory based model for artificial neural networks, and it requires the use of optimisation algorithms for ...descent learning algorithms may ...
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Improved spikeprop algorithm for neural network learning
... 1.5 Scope of the Study 1. Using C++ program to develop several SNN Models, each having its own learning characteristic. It has been established that in order for SNN to perform classification accurately and ...
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Artificial Neural Network Learning Techniques: A Survey
... Artificial Neural Networks for their drastic development in most of the ...Artificial Neural Network is a computation based model hinged on the framework and functions of neural ...through ...
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Particle swarm algorithm in neural network learning
... Za prvi slučaj prvog problema vidljivo je iz tablice (Tablica 5.) kako učenje mreže sa PSO algoritmom već nakon 100 iteracija postiže puno manju grešku, za razliku od uče[r] ...
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Recurrent Neural Network Learning of Phonological Regularities in Turkish
... have only simple detectors for phonological natural classes such as consonant and vowels; i.e the network is able to use the recurrent links to encode complex tempo[r] ...
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Particle swarm optimization for neural network learning enhancement
... NN learning is called Backpropogation (BP) ...BP learning is basically a hill climbing technique, it runs the risk of being trapped in local minima where every small change in synaptic weight increases the ...
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BACTERIAL FORAGING OPTIMIZATION ALGORITHM FOR NEURAL NETWORK LEARNING ENHANCEMENT
... feedforward neural network to optimize the learning process in terms of convergence rate and classification accuracy but this process needs longer training ...feedforward neural network ...
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Algorithm animation and its application to artificial neural network learning
... examining two relatively unexplored aspects of algorithm animation: issues of view design effectiveness and its application to a different type of algorithm, namely back-propagation arti[r] ...
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Deep Learning in an Adaptive Function Neural Network
... Artificial neural network learning is typically accomplished via adaptation between ...the network can learn to respond differentially to classes of incoming ...internal neural ...
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Modal Learning in a Neural Network
... 5. Conclusions and Future Work In conclusion, the snap-drift algorithm has shown potential in phrase recognition. The results show the learning of the SDNN is fast, stable and reliable in recognizing the input ...
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A Learning Algorithm For Neural Network Ensembles
... {navone,verdes,granitto,ceccatto}@ifir.edu.ar Abstract The performance of a single regressor/classifier can be improved by combining the outputs of several predictors. This is true provided the combined predictors are ...
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Learning of N-layers neural network
... KONEČNý, V., MATIáŠOVá, A., RáBOVá, I.: Learning of N-layers neural network. Acta univ. agric. et silvic. Mendel. Brun., 2005, LIII, No. 6, pp. 75–84 In the last decade we can observe increasing ...
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Hybrid Neural Network Architecture for On Line Learning
... use neural networks for generalization but they do so as signal processing black ...hybrid neural network ar- chitecture that uses two kinds of neural networks simultaneously to consider ...
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Convolutional neural network as an architecture for deep learning
... Deep learning Deep Learning is a new area of Machine Learning research that learns features and tasks directly from ...on learning data representations. Learning can be supervised, ...
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Continuous Learning in a Hierarchical Multiscale Neural Network
... In our experiments, the perplexity could not be improved by using a RNN meta-learner or a deeper meta-learner. We hypothesis that this may be caused by several reasons. First, storing a hid- den state in the meta-learner ...
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Deep Learning Binary Neural Network on an FPGA
... ral network designed to address pattern recognition problems for real-time and low power embedded ...binary neural network is pro- ...deep neural network. Reconfigurability of FPGA ...
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Recurrent Neural Network (CNN) Deep Learning
... • If we drop some units in the recurrent layer randomly, then it will be detrimental for learning. • Because the same units are applied to each input in a sequence. • Randomly switching this units on and off will ...