The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weigh...

authentic record of my own work carried under the supervision of Mr. SANJAY SHARMA and Mrs. MANU BANSAL at TIET, PATIALA. The matter presented in this thesis has not been submitted in any other University or Institute for the award of Master of Engineering. Signature of the student This is certified that the above statement made by the candidate is correct to the best of my knowledge. ACKNOWLEG...

The effect of the coefficients used in the conventional back propagation algorithm on training connectionist models is discussed, using a vowel recognition task in speech processing as an example. Some weaknesses of the use of fixed coefficients are described and an adaptive algorithm using variable coefficients is presented. This is found to be efficient and robust in comparison with the fixed...

| A variation of the classical Back{Propagation algorithm for neural network training is proposed and convergence is established using the perturbation results of Mangasarian and Solodov 1]. The algorithm is similar to the Successive Overrelaxation (SOR) algorithm for systems of linear equations and linear complementary problems in using the most recently computed values of the weights to updat...

Back propagation(BP) is used to solve real world problems which use the concept of multilayer perceptron(MLP). BP have the disadvantage of trapped in local minima, slow convergence rate and more error prone. To optimize BP Algorithm, Particle swarm optimization(PSO) and Genetic algorithm(GA) is used. Limitation of PSO_Hill and PSO_A* is overcomes when these algorithms are combined and on the ba...

The objective of this paper is to a design of pattern classification model based on the back-propagation (BP) algorithm for decision support system. Standard BP model has done full connection of each node in the layers from input to output layers. Therefore, it takes a lot of computing time and iteration computing for good performance and less accepted error rate when we are doing some pattern ...

The standard back propagation algorithm for training artificial neural networks utilizes two terms, a learning rate and a momentum factor. The major limitations of this standard algorithm are the existence of temporary, local minima resulting from the saturation behaviour of the activation function, and the slow rates of convergence. Previous research demonstrated that in ‘feed forward’ algorit...

The aim of this study was to estimate suspended sediment by the ANN model, DT with CART algorithm and different types of SRC, in ten stations from the Lorestan Province of Iran. The results showed that the accuracy of ANN with Levenberg-Marquardt back propagation algorithm is more than the two other models, especially in high discharges. Comparison of different intervals in models showed that r...

Tomography is the three-dimensional reconstruction of an object from images taken at different angles. The term classical tomography is used, when the imaging beam travels in straight lines through the object. This assumption is valid for light with short wavelengths, for example in xray tomography. For classical tomography, a commonly used reconstruction method is the filtered back-projection ...