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In this paper, based on investigating and analyzing on the affecting factors for support type of development roadways as well as the successful support cases in Chengchao Iron mine, the improved BP neural network is put forward to study on the support type of development roadways. It may be seen from the learning course of learning samples and the prediction results of support types that whether the...
One of the main obstacles to reliable communications is the inter symbol interference. An adaptive equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. The conventional way to combat with ISI is to include an equalizer in the receiver. This paper presents a new approach to equalization of communication channels...
Premature convergence is the main obstacle to the application of genetic algorithm. This paper makes improvement on traditional genetic algorithm by linear scale transformation of fitness function, using self-adaptive crossover and mutation probability and adopting close relative breeding avoidance method. Simulation results show that the improved algorithm outperforms traditional genetic algorithm...
SOM neural network is one of the most commonly used Clustering algorithm in the text clustering. The initial connection weights of SOM neural network will affect the degree of convergence. If the Initial connection weights are not set appropriate, that will cause in a long wandering around the local minimum, accordingly lower the speed of convergence, or even cause local convergence or not convergence...
Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
Evaluation of certain properties of calcined alumina or special grade alumina is necessary and important to its manufactures. Generally it is determined in the laboratories using different instrumental and manual methods, which is cost and time intensive. In the present work, evolving neural network has been used for the estimation of a property given few others. To evolve the neural network model...
Three term backpropagation (BP) network as proposed by Zweiri in 2003 has outperformed standard two term backpropagation. However, further studies on three term backpropagation in 2007 indicated that this network only surpassed standard BP for small scale datasets but not for medium and large scale datasets. It has also been observed that by using mean square error (MSE) as a cost function in three...
Different from gradient-based dynamics (GD), a special class of neural dynamics has been found, developed, generalized and investigated by Zhang et al, e.g., for online solution of time-varying and/or static nonlinear equations. The resultant Zhang dynamics (ZD) is designed based on the elimination of an indefinite error-function (instead of the elimination of a square-based positive or at least lower-bounded...
This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods...
This paper explore the swarm intelligence stability based on stochastic diffusion search (SDS) which is capable to find rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of the positive feedback...
Because there were a lot of facts that affect the intensity of coal and gas outburst, a BP neural network model for forecasting the intensity was constructed. Aimed at the shortcoming of the BP neural network, such as the slow training speed, easy to be trapped into the local optimums, and the premature convergence of genetic algorithm (GA) BP neural network, a method to design the BP neural network...
This paper studies various training algorithms of BP neural network and proposes an improved conjugate gradient algorithm which combines conjugate gradient algorithm with inexact line search route based on generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied...
Based on neural network, an improvement scheme that iterative matrix replace secondary derivative has been developed by introduced quasi-Newton algorithm. Profile code based on probability has been used and comparison of window width and learning training has been completed. The experiment results indicate that the prediction for secondary structures of protein obtain a very good effect based on neural...
PID neural network controller design for Sharif University of Technology (SUT) building energy management system (BEMS) is addressed in this paper. The most important characteristics of process systems are time delay with model uncertainties. Artificial neural networks can perform adaptive controller properties through learning processes. PID neural network has the advantage of both conventional PID...
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