Neural Nets

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Related to neural network: Artificial neural network

Neural Nets

Models which mimic the massive parallel processing that occurs in the brain.

Neural Net

A computerized system designed to imitate human thought. Specifically, neural nets are designed to make or recommend investment decisions in light of changing circumstances in a market. Using neural nets has become more common as computers have become more accessible.
References in periodicals archive ?
Simulation of Chest Diseases Using Competitive Neural Network (CpNN).
The cryptanalysis method based on neural network utilizes the learning ability of the neural network to train the neural network with the known Ming ciphertext.
The objective of the training is to find an optimum answer of neural network. Figure 5 shows the best training performance that developed neural network with 2 hidden layers after 300 epochs.
The predicted value is a linear combination of the known training values, in a probabilistic neural network approach.
In current assay, three neural network topologies were tested to define the best performance for chemical coagulant in two dosage points.
In recent years, dynamical neural networks have been extensively studied due to their potential applications in image processing, control theory, pattern recognition, associative memories, optimization problems.
Employing the fuzzy uncertainty approach [21], neural network model (5) can be expressed as follows:
The finance sector uses a mix of data acquisition, data preprocessing, and implementation of neural network models for making accurate predictions in the stock market.
The Multilayer Perceptron Neural Network Model is used and the network architecture is given in Figure 1 of Appendix.
An artificial neural network can be thought of as a black box with a number of knobs.
The innovation uses computational neural networks, a form of artificial intelligence, to "learn" how a nanoparticle's structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples.
Shane actually specializes in optics but plays around with neural networks on the side.

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