this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

Short Term Load Forecasting (STLF) is a power system operating procedures that have an important role in terms of realizing the economic electric production. This research focuses on the application of hybrid PSO-ANN algorithm in STLF. Load data grouped by the type of weekdays and holidays. Consumption of electricity load in West Java Indonesia, used as input to the learning algorithm PSO-ANN. ...

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descripti...

This paper presents a novel learning algorithm for training and constructing a Radial Basis Function Neural Network (RBFNN), called MuPSORBFNN algorithm. This algorithm combines Particle Swarm Optimization algorithm (PSO) with mutation operation to train RBFNN. PSO with mutation operation and genetic algorithm are respectively used to train weights and spreads of oRBFNN, which is traditional RB...

In this paper we propose a novel hybrid algorithm (GA/PSO) combining the strengths of particle swarm optimization with genetic algorithms to evolve the weights of recurrent neural networks. Particle swarm optimization and genetic algorithms are two optimization techniques that have proven to be successful in solving difficult problems, in particular both can successfully evolve recurrent neural...

To overcome the flaws of traditional BP learning algorithm of its low convergence speed and easy falling into local extremum during turbo-generator vibration faults diagnosis, a novel algorithm called PSO-BP is proposed for artificial neural network (ANN) learning based on particle swarm optimization (PSO) in this paper. The algorithm covers the two phases. Firstly, PSO algorithm is applied to ...

This paper deals with an indoor propagation problem where it is difficult to rigorously obtain the field strength distribution. We have developed a propagation model based on a neural network, which has advantages of deterministic (high accuracy) and empirical (short computation time) approaches. The neural network architecture, based on the multilayer perception, is used to absorb the knowledg...

Abstract: One of the equipment that can help improve distribution system status today and reduce the cost of fault time is remote control switches (RCS). Finding the optimal location and number of these switches in the distribution system can be modeled with various objective functions as a nonlinear optimization problem to improve system reliability and cost. In this article, a particle swarm ...

The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and str...

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, variety of techniques have been employed for electrical forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due their ability adapt properly the consuming load's hidden characteristic. Therefore, these were widely accepted by researchers. As parameters neural n...