Computational Methods in Engineering

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Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural network and its energy function is introduced. This network requires a significantly smaller number of neurons compared to its counterparts. Also presented is the performance of this neural network. Keywords: Routing, Multicomputer systems, Recurrent neural networks, Mesh interconnection networks.

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Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determined by a sufficiently rapid and precise method. In this paper, a new algorithm based on assigning a vector to each power system bus is presented. These vectors indicate buses conditions from the viewpoint of voltage stability. In this new method, using the clustering methods such as kohonen neural network, fuzzy C-Means algorithm and fuzzy kohonen algorithm, voltage control areas are determined The proposed method has advantages such as determining PV and PQ buses which belong to the weak area simultanously, under all operating conditions and without a need to system model. Also by comparing the results of applying clustering methods, it has been observed that, due to simplicity of implementation and precision of the results, the two dimensional kohonen neural network is a more suitable tool for clustering power system to voltage control areas than the fuzzy C-Means and fuzzy kohonen methods. Keywords: Voltage stability, Voltage weak area, Voltage control area, Corrective control, Pattern recognition, Kohonen neural network, Fuzzy C-Means algorithm, Fuzzy Kohonen algorithm.

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In this research, behaviour of clayey soils under triaxial loading is studied using Neural Network. The models have been prepared to predict the stress-strain behaviour of remolded clays under undrained condition. The advantage of the model developed is that simple parameters such as physical characteristics of soils like water content, fine content, Atterberg limits and so on, are used to model the stress-strain behaviour of clays under triaxial loading, without performing exact and time-consuming tests on samples. Results from the network show that neural network is a good tool for prediction of stress-strain behaviour of clayey soils using simple physical characteristics of such soils

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In this study, colorimetric calibration of scanner has been done via perceptron neural network with three or four layers by back propagation algorithm for colored polyester fabrics. The results obtained for random training samples are not satisfactory but application of selective training samples for L*a*b* or RGB leads to good results, with better results obtained for the L*a*b* method. On the other hand, the color differences between calculation XYZ and real XYZ for unknown samples, are not only in agreement with the results of polynomials and regression methods, but are also better than the results obtained in previous studies where neural networkhad been used for colorimetric calibration of scanner.

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Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of cotton-covered nylon core yarns. Multilayer Feedforward network with Back Propagation learning algorithm was used to study the relationship and mapping among the process parameters, i.e. count of sheath part, count of core part, applying pretension to the core part, inserted twist to the core spun-yarn as well as tensile properties, i.e. breaking strength and breaking elongation. The results show that ANN is an effective method for the prediction of the tensile properties of these yarns. This is due to the fact that in each case, standard deviation of prediction error for test and train data was less than that obtained from the expreiments.

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This paper presents a method based on neural networks to detect broken rotor bars and end rings in squirrel cage induction motors. In the first part, detection methods are reviewed and traditional methods of fault detection as well as dynamic model of induction motors are introduced using the winding function method. In this method, all stator and rotor bars are considered independently in order to check the performance of the motor for any faults in the parts. Then the frequency spectrum of current signals is derived using the Fourier transform and analyzed under various conditions. In the second part of the paper, an analytical discussion of the theoretical principles is presented to arrive at a simple algorithm for fault detection based on neural networks. The neural network has been trained using the information from a 1.1 KW induction motor. Finally, the system is tested with different values of load torque and is found capable of working on-line to detect all normal and ill-performing conditions.

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In welded tubular joints, when the fatigue crack depth is less than 20% of chord wall thickness, the crack growing process is highly affected by weld geometry. Hence, T-butt solution and weld magnification factor (Mk) are applicable tools for evaluating the crack growth rate in this domain. In this research, the capability of Artificial Neural Network (ANN) for estimating the Mk of weld toe cracks in T-butt joints is investigated. Four Multi-Layer Perceptron (MLP) networks are designed and trained to predict the Mk in deepest point and ends of weld toe cracks under membrane and bending stresses. Training and testing data of networks are extracted from a reputable resource on finite element modeling. Comparison of the results obtained and those from the most recently published equations shows that using ANN seems to be very beneficial in this field

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Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.

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Many efforts have been done in recent years to decrease the required time for analysis of FKP (Forward Kinematics
Problem) of parallel robots.This paper starts with developing kinematics of a parallel robot and finishes with a suggested
algorithm to solve forward kinematics of robots. In this paper, by combining the artificial neural networks and a 3rd-order
numerical algorithm, an improved hybrid strategy is proposed in order to increase the accuracy and speed of forward kinematics
analysis of parallel manipulators. First, an approximate solution of the forward kinematics problem is produced by the neural
network. This approximate solution is then considered as the initial guess for the 3rd-order Newton-Raphson numerical
technique. By applying Stewart-Gough parallel manipulator, the efficiency of the proposed method is evaluated. It is shown that
replacing the Newton-Raphson algorithm by the 3rd-order one leads to a reduction of the iterations required to reach the desired
accuracy level and thus a reduction of the FKP analysis time. Finally, Stewart robot is used to simulate the movement of jaw.
This novel algorithm can be applied to any forward kinematics of serial or parallel robots.

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Financial crises in banking systems are due to inability to manage credit risks. Credit scoring is one of the risk management techniques that analyze the borrower's risk. In this paper, using the advantages of computational intelligence as well as soft computing methods, a new hybrid approach is proposed in order to improve credit risk management. In the proposed method, for modeling in uncertainty conditions, parameters of the neural network, including weights and errors, are considered in the form of fuzzy numbers. In this method, the underlying system is firstly modeled using neural networks and then, using fuzzy inferences, the optimal decision will be determined with the highest degree of superiority. Empirical results of using the proposed method indicate the efficiency and high accuracy of this method in analyzing credit rating problems.

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The behavior of many types of fluids can be simulated using differential equations. There are many approaches to solve differential equations, including analytical and numerical methods. However, solving an ill-posed high-order differential equation is still a major challenge. Generally, the governing differential equations of a viscoelastic nanofluid are ill-posed; hence, their solution is a challenging task. In addition, the presence of very tiny nanoparticles (lower than 100 nm) induces new heat and mass transfer mechanisms which can increase the complexity of the behavior of the viscoelastic nanofluids. Therefore, creating or developing new analytical or semi-analytical approaches to solve the governing equations of these types of nanofluids is highly demanded. In the present study, by using a new idea and utilizing an optimization approach, a new solution approach has been presented to solve the governing equations of viscoelastic nanofluids. By using the optimization method, a basic initial guess was changed toward an optimized solution satisfying all boundary conditions and the governing equations. The results indicate the robustness and accuracy of the presented method in dealing with the high-order ill-posed governing differential equations of viscoelastic nanofluids.