Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...

In this paper, the application of soft computing techniques in prediction of an occupant’s behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into tempo...

Elman Networks is a one of the dynamic recurrent neural networks. In this research it is used for the prediction of surface roughness in Electrical Discharge Machining (EDM). Training of the models was performed with data from series of EDM experiments on SKD 11 (AISI D2) Tool steel; in the development of predictive models, machining parameters of discharge current, pulse duration and duty cycl...

Contract Bridge is an intelligent game, which enhances the creativity with multiple skills and quest to acquire the intricacies of the game, because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined, since the outcome at any intermediate stage is purely based on the decision made on t...

This paper presents some considerations regarding the prediction of the electrical energy consumption. It is well known that the central element of a microeconomic analysis is represented by the economical agents actions, actions that follow their own interest such as: the consumer – maximization of his satisfaction, the producer – maximization of his profit. The study case is focused on the pr...

Methods of identifying of highly nonlinear processes defined by a small data set are presented in this paper. Neural networks of different structures are implemented on two types of data set in order to get corresponding nonlinear models. Two layers NN based on Levenberg-Marquardt algorithm is used, in the first part of the paper, in process of prediction. Trend of the considered highly nonline...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on an improved particle swarm optimization. The proposed algorithm computes concurrently both the evolution of network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network. Thereafter, we introduce and discuss a novel control metho...

Ambient air temperature prediction is of a concern in environment, industry and agriculture. The increase of average temperature results in natural disasters, higher energy consumption, damage to plants and animals and global warming. Ambient air temperature predictions are notoriously complex and stochastic models are not able to learn the non-linear relationships among the considered variable...