In real field demining, soldiers can only judge the landmine location and type by the sound generated from a landmine detector. Therefore, a virtual landmine detection training system can replicate the sound in a realistic manner is imperative. In this paper, several sound datasets for various targets have been collected. Each dataset contains about 500 instances, each representing a different ...

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization (PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to o...

This paper presents a method for data model partitioning of power distribution network. Modern Distribution Management Systems which utilize multiprocessor systems for efficient processing of large data model are considered. The data model partitioning is carried out for parallelization of analytical power calculations. The proposed algorithms (Particle Swarm Optimization (PSO) and distributed ...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clus...

This paper builds a soft sensor model based on a PSO-BP neural network for titanium billet heating furnace-temperature. An improved particle swarm optimization algorithm is proposed. This algorithm is used to optimize the initial weights of the neural network, which can overcome the disadvantages of the random initial weights of the conventional BP neural networks. The proposed algorithm is bas...

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity...

This paper focusses on a discrete-time neural identifier applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with a novel algorithm based on extended Kalman filter (EKF) and par...

multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...