Computer Simulation of Neural Networks for Perceptual Psychology

1992 ◽  
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Perceptual Psychology

Some tricks of the trade

2017 ◽  
Author(s):  
Michael P. Allen ◽  
Dominic J. Tildesley
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
General Structure ◽  
Tips And Tricks ◽  
Linked Lists

This chapter concentrates on practical tips and tricks for improving the efficiency of computer simulation programs. This includes the effect of using truncated and shifted potentials, and the use of table look-up and neural networks for calculating potentials. Approaches for speeding up simulations, such as the Verlet neighbour list, linked-lists and multiple timestep methods are described. The chapter then proceeds to discuss the general structure of common simulation programs; in particular the choice of the starting configuration and the initial velocities of the particles. The chapter also contains details of the overall approach to organising runs, storing the data, and checking that the program is working correctly.

An Improved Adaptive Neural Network Detecting Approach to Harmonic Currents with Changeable Learning Parameters

2012 ◽  
Vol 433-440 ◽  
pp. 6546-6550
Author(s):  
Jun Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Simulation ◽  
Active Power ◽  
Adaptive Neural Network ◽  
Active Power Filters ◽  
Adaptive Weights ◽  
Power Filters ◽  
Harmonic Currents ◽  
Adaptive Noise

Using the adaptive noise canceling technology, this paper proposes a new detecting approach to harmonics and reactive currents based on neural networks with changeable learning parameters. The structure of this neural network and the adaptive weights adjusting algorithm are presented. The contradiction of the detecting speed and the precision has been settled preferably. The proposed detecting approach can be used for detecting the harmonics and the reactive currents of active power filters. The results of the theoretical analysis and computer simulation confirm the validity of the approach.

Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

1985 ◽  
Vol 123 (4) ◽  
pp. 215-273 ◽  
Author(s):  
John W. Clark ◽  
Johann Rafelski ◽  
Jeffrey V. Winston
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Neuronal Interactions

scholarly journals Neural networks’ learning process acceleration

2020 ◽  
pp. 313-321
Author(s):  
L. Katerynych ◽  
◽  
M. Veres ◽  
E. Safarov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Learning Process ◽  
Parallel System ◽  
Artificial Neural

This study is devoted to evaluating the process of training of a parallel system in the form of an artificial neural network, which is built using a genetic algorithm. The methods that allow to achieve this goal are computer simulation of a neural network on multi-core CPUs and a genetic algorithm for finding the weights of an artificial neural network. The performance of sequential and parallel training processes of artificial neural network is compared.

scholarly journals Neural Networks for Tested MG Artillery Fuses

2019 ◽  
Vol 49 (4) ◽  
pp. 157-186
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Evaluation Data ◽  
Technical Parameters ◽  
Artificial Neural ◽  
Learning Test ◽  
The Way

Abstract The article presents the information about the usage of artificial neural networks. The automation process of neural networks of the analysed evaluation data results is highlighted. The kinds of MG type artillery fuses are described and the kinds of cartridges’ calibres, in which they are used, are also specified. The way of preparation of databases of test results to computer simulation is described. Building of neural networks determining the main technical parameters and sizes of learning, test and validation sets is characterized. The summary for chosen active neural networks for individual kinds of the analysed MG type artillery fuses is presented. Graphs of learning, values of sensibility indicators and fragments of prediction sheets for the chosen neural networks were shown.

scholarly journals New Stability Criterion for Fractional-Order Quaternion-Valued Neural Networks Involving Discrete and Leakage Delays

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Bingjun Li ◽  
Bingnan Tang
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Fractional Order ◽  
Stability Criterion ◽  
Uniform Stability ◽  
Sufficient Criterion ◽  
Leakage Delays ◽  
Analysis Strategy ◽  
Set Up ◽  
Mapping Theory

In the current work, we are devoted to the issue of uniform stability of fractional-order quaternion-valued neural networks involving discrete and leakage delays. Making use of the contracting mapping theory, we prove that the equilibrium point of the involved fractional-order quaternion-valued neural networks exists and is unique. Taking advantage of mathematical analysis strategy, a sufficient criterion involving delay to verify the global uniform stability for the considered fractional-order quaternion-valued neural networks is set up. Computer simulation figures are displayed to sustain the rationality of the established conclusions. This study generalizes and supplements the research of Xiu et al. (2020).

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