Recently, in classification of multispectral remote resensing image by using back-propagation neural network (BPNN), all bands of image must be used for training and classing. Disadvantage of the mentioned method not only requires more time for training and classing but also more complexity. In this paper, to decrease the mentioned disadvantage, principal component analysis (PCA) is applied to ...

Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper pr...

Research in assembly planning can be categorised into three types of approach: graph-based, knowledgebased and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this s...

A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-...

Owing to the heterogeneity of software and hardware in different types mobile terminals, received signal strength indication (RSSI) from same Wi-Fi access point (AP) varies indoor environments, which can affect positioning accuracy fingerprint methods. To solve this problem consider nonlinear characteristics propagation attenuation, we propose a whale optimisation algorithm-back-propagation neu...

In the recent years, computer vision has emerged as a prospective field, related to the recognition of the object by a computer or machine. In the presented work, the application of computer vision to extract the features of a pea is explored. Feature related to pea are shape, texture, and color. The present work analyzes the object, on the basis of surface areas of the pea, computed from diffe...

Cellular neural networks (CNN) were introduced by Chua and Yang in 1998 [1]. The idea of the CNN was inspired from the architecture of the cellular automata and the neural networks. Unlike the conventional neural networks, the CNN has local connectivity property. Since the structure of the CNN resembles the structure of animals retina, the CNN can be used for various image processing applicatio...

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...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

In order to solve the problem of poor control performance, caused by fixed parameters active disturbance rejection (ADRC) in bearingless permanent magnet synchronous motors (BPMSM), a dynamic adjustment method ADRC, based on genetic algorithm and back-propagation neural network (GA-BPNN), is proposed. Firstly, ADRC models motor-side suspension-side are established, according motor speed formula...