Learning in neural networks

Learning in the context of a neural network is the process of adjusting the weights and biases in such a manner that for given inputs, the correct responses, or outputs are achieved. Learning algorithms include  

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Oja, E., Unsupervised neural learning, in Neural Networks for Chemical Engineers (A. B. Bulsari, (ed.). Elsevier, Amsterdam, 1995, p. 21. 

Learning in neural networks happens by associative or competitive learning laws. In this context, learning means the following ... 

D. Nguyen and B. Windrow, The Truck Backer-Upper An Example of Self-Learning in Neural Networks, in International Joint Conference on Neural Networks, IJCNN, IEEE, Washington, EX3, 1989, vol. 2, p. 357. 

J. Devillers (Ed), Neural Networks in QSAR and Drug Design, Academic Press, London, 1996 S. Haykin, Neural Networks, Macmillan, New York, 1994 R. Neal, Bayesian Learning for Neural Networks, Springer, New York, 1996. 

Luo [86] proposed a kind of neural cluster structure embedded in neural networks. The ANN is based on the error back-propagation learning... 

Alexandras Koulouris, Bhavik R. Bakshi and George Stephanopoulos, Empirical Learning through Neural Networks The Wave-Net Solution Bhavik R. Bakshi and George Stephanopoulos, Reasoning in Time Modeling, Analysis, and Pattern Recognition of Temporal Process Trends... 

Supervisedleaming algorithms Learning algorithms often used in neural networks that use the output error of the neural network as a training signal. 

Barto, A.G. 1990. Connectionist learning for control an overview. In Neural Networks for Control. 

In essence, the output of the feedback controller is an indication of the mismatch between the dynamics of the plant and the inverse-dynamics model obtained by the neural network. If the true inverse-dynamic model has been learned, the neural network alone wiU provide the necessary control signal to achieve the desired trajectory [118,120],... 

In neural net jargon, the neuron is known as a perceptron (Rosenblatt, 1958). The learning rule for these multilayer perceptrons is called the back-propagation rule. This is usually ascribed to Werbos in his thesis of 1974 (Werbos, 1993), but was popularized by Rumelhart and McClelland (1986) as recently as 1986, since when there has been a revival in interest in neural networks. 

Kovalishyn et al. used the cascade correlation neural net to select variables in QSAR smdies (131). Their results suggest that these pmning methods can be successfully used to optimize the set of variables for the cascade-correlation learning algorithm neural networks. The use of variables selected by the elaborated methods provides an improvement of nemal network prediction ability compared to that calculated using the unpruned sets of variables. 

Luo proposed a kind of neural cluster structure embedded in neural networks. The ANN was based on the error back-propagation learning algorithm. The predictive ability of the neural cluster structure was compared with that of common neural net structures. A comparison of predictability with four neural networks was presented and they were applied to correct for matrix effects in XRF. 

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