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In this paper we propose a method for continuously processing and learning from data in Restricted Boltzmann Machines (RBMs). Traditionally, RBMs are trained using Contrastive Divergence (CD), which is an algorithm consisting of two phases, of which only one is driven by data. This not only prohibits training of RBMs in conjugation with continuous-time data streams, especially in event-based real-time...
It is widely known that particle swarm optimization (PSO) has some drawbacks, especially it loses diversity easily. In order to solve this problem, some improved PSOs were proposed which update velocity according to diversity. However, some important information about particles is still not sufficiently utilized such as fitness values. As a gradient descent method, backpropagation (BP) algorithm is...
A new technique for the training of ANNs is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using differential evolution method, then further being trained by Levenberg Marquardt method. The processed data are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The hybrid training...
This paper presents a new algorithm called Feature Selection Age Layered Population Structure (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS is a modification of Hornby's ALPS algorithm — an evolutionary algorithm renown for avoiding pre-mature convergence on difficult problems. FSALPS uses a novel frequency count system to rank features in the...
This paper outlines the use of an Evolutionary Algorithm (EA) to perform the Equalisation of a non minimum phase channel. Conventional techniques utilising first and second order approximations of the error surface, have been demonstrated to be ineffective in achieving an optimal solution in continuous simulations, and have proved incapable of dealing with the more difficult non minumum phase problems...
Aiming at the problem that difficulty of expression of the temporal accumulation in the time series prediction using artificial neural network, a prediction method which uses the process neural network is presented. The algorithm of quantum particle swarm is designed which has double chain structure and is used to train the process neural network. The algorithm used quantum bits to construct chromosomes...
The artificial bee colony algorithm is a novel simulated evolutionary algorithm. The artificial bee colony algorithm has positive feedback, distributed computation and a constructive greedy heuristic convergence. Back propagation is a kind of feed forward neural network widely used in many areas, but it has some shortcomings, such as low precision solutions, slow search speed and easy convergence...
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