We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ...

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due...

In this paper, a hybrid swarm system is presented for time series forecasting. It consists of an intelligent hybrid model composed of an Arti cial Neural Network (ANN) and a Particle Swarm Optimizer (PSO), which search the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling o...

Designing the architecture and correct parameters for the learning algorithm is a tedious task for modeling an optimal Artificial Neural Network (ANN), which is smaller, faster and with a better generalization performance. In this paper we explain how a hybrid algorithm integrating Genetic algorithm (GA), Simulated Annealing (SA) and other heuristic procedures can be applied for the optimal des...

In traditional help desk service centres of manufacturing companies, diagnosis of machine faults relies heavily on the service engineers’ knowledge and experience. This method poses a problem of training and maintaining a pool of expert service engineers. With the advancement of Internet technology and artificial intelligence techniques, it is possible to deliver online customer service support...

Considering the importance of Cd and U as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the Eshtehard region in Iran by means of a developed artificial neural network (ANN) model. The forward selection (FS) method is used to select the input variables and develop hybrid models by ANN. From 45 input candidates, 13 and 14 ...

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the Uni...

Artificial Neural Network (ANN) is the primary automated AI system preferred for medical applications. Even though ANN possesses multiple advantages, the convergence of the ANN is not always guaranteed for the practical applications. This often results in the local minima problem and ultimately yields inaccurate results. This convergence problem is common among ANNs and especially in Kohonen ne...

The purpose of this paper is to explore the ability to continually change and obtain new understanding of driving power of Expert Approach. Expert systems are able to advantageously replace human experts in various application areas with the involvement of ANN and Fuzzy Logic. Finally this hybrid system is formed to gain the outstanding performance from a expert system. The basic concept of ANN...

Pneumatic Artificial Muscle (PAM) actuator has been widely used in medical and rehabilitation robots, owing to its high power-to-weight ratio and inherent safety characteristics. However, the PAM exhibits highly non-linear and time variant behavior, due to compressibility of air, use of elastic-viscous material as core tube and pantographic motion of the PAM outer sheath. It is difficult to obt...