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Download High Performance Programming for Soft Computing by Oscar Montiel Ross, Roberto Sepulveda Cruz PDF

By Oscar Montiel Ross, Roberto Sepulveda Cruz

This publication examines the current and way forward for delicate desktop concepts. It explains how you can use the most recent technological instruments, comparable to multicore processors and portraits processing devices, to enforce hugely effective clever approach equipment utilizing a basic objective laptop.

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The Perceptron. It is a nonlinear neuron known as the McCulloch-Pitts model of a neuron. It uses as activation function the hard limiter sign function, providing a binary output. The output yk is used to perform the training, so it only provides information to know if the obtained output value is wrong or not. The error surface is very rough (very steep) because of the binary output that the activation function provides. 2. The ADALINE. It typically uses bipolar activation function for its input signals and target output.

The McCulloch-Pitts model can enable or disable using a probabilistic decision; the neuron can only reside in one of the two states: +1 or –1. 20 High Performance Programming for Soft Computing The problems that a simple neuron can solve are very limited, therefore, to solve complex problems it is necessary to join several neurons to form a specific architecture known as ANN. It consists of massively parallel distributed processors that enable the ANN to store knowledge, allowing us to use it when it is required.

2. The ADALINE. It typically uses bipolar activation function for its input signals and target output. The output of the linear combiner vk is used to achieve the training; hence the output in the training phase is linear. The output provides real numbers in the valid output range. The error surface is smooth. 3. The modern artificial neuron. It is in essence a perceptron that uses a nonlinear smooth function as the activation function, in contrast with the binary output of the McCulloch-Pitt model.

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