Search

Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field. [ABSTRACT FROM AUTHOR]

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by Particle Swarm Optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black–Scholes model. [ABSTRACT FROM AUTHOR]

Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimization problems which directed by the fitness values. In this paper, we propose one efficient fractional global learning machine (Fragmachine) which includes two stages (descending and ascending) to determine the optimal search path. The neural network model is used to approach the given fitness value. Specifically, for the descending stage, the integer gradient of the network output with respect the current location is employed to find the next descending point, while for the ascending stage, the fractional gradient is implemented to climb and escape from the local optimal point. We further propose one adaptive learning rate during training which relies on both the current gradient (integer or fractional) information and the fitness value. Finally, a series of numerical experiments verify the effectiveness of the proposed algorithm, Fragmachine. [ABSTRACT FROM AUTHOR]

Multitudinous parameters involved have made the direct methanol fuel cell (DMFC) a complex “black-box,” posing challenges and difficulties in its modeling. This paper presents a neural network (NN) model with immune-based particle swarm optimization (IPSO) approach of the DMFC system, which is different from the conventional complex mathematical models. With the actual operation of DMFC taken into consideration, the polarization curves are run under a stable condition as the reference data for training the model. To reduce time cost for the training procedure and maintaining minimum modeling error, the IPSO algorithm is applied to the learning procedure of NN model. By combining the NN and the IPSO, the weight of the transfer function on the node in the hidden layer can be adjusted to minimize modeling error. The simulation results were in agreement with the experimental results, showing that the hybridization of NN model with IPSO approach can effectively demonstrate the polarization behaviors on a DMFC system. Therefore, this hybrid NN model with IPSO approach can be used as a simulation tool, which can save much money and time for reforming the conventional mathematical models with expensive experiment. Furthermore, the proposed method reveals an adaptive ability to improve the model even if the DMFC system structure is different. [ABSTRACT FROM PUBLISHER]

This paper deals with an indoor propagation problem where it is difficult to rigorously obtain the field strength distribution. We have developed a propagation model based on a neural network, which has advantages of deterministic (high accuracy) and empirical (short computation time) approaches. The neural network architecture, based on the multilayer perception, is used to absorb the knowledge about the given environment through training based on measurements. Such network then becomes capable to predict signal strength that includes absorption and reflection effects without additional computation and measurement efforts. The neural network model is used as a cost function in the optimization of the base station location. As optimization algorithm we have applied the particle swarm optimization (PSO) algorithm, i.e. a global optimization routine based on the movement of particles and their intelligence. Appropriate PSO parameters are discussed in the paper, and the results of PSO are compared with results obtained with two standard algorithms such as simplex optimization method and Powell's conjugate direction method. [ABSTRACT FROM AUTHOR]

Industrial asphalt foaming is a process of mixing, transferring heat and phase transition of multi-phase multi-component flow in the expansion chamber, including a series of complex problems such as difficult analysis and observation. In the context of this research, asphalt foaming quality control model using neural network was constructed to map the pertinent parameters (asphalt temperature, water content and air pressure) into the foaming properties (maximum expansion ratio and half-life time). In addition, Particle swarm optimization was adopted to avoid the local infinitesimal defect and slow constringency in the traditional BP algorithm. The prediction error of Shell 60/70 quality control model indicates that the asphalt foaming quality control model using PSO-BP neural network is effective, and this provides a novel idea for studying of the foam asphalt, which has practical significance for the design of asphalt foam equipment. Finally, a parameter optimization model with the maximization of the foaming index was proposed based on the quality control model to improve the foaming performance of the asphalt foaming equipment. With the analysis of a parameter optimization model of Shell 60/70 asphalt, the better foaming properties are the maximum expansion ratio of 12.28 times and half-life time of 11.02 s at the foaming condition of asphalt temperature of about 168 °C, the water content of about 1.5 wt% and the air pressure of about 1 bar. The method proposed in this paper is of important reference significance for the engineering with difficult analysis and observation. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel approach of permeability prediction by combining cuckoo, particle swarm and imperialist competitive algorithms with Levenberg–Marquardt (LM) neural network algorithm in one of heterogeneous oil reservoirs in Iran. First, topology and parameters of the Artificial Neural Network (ANN) as decision variables were designed without the optimization method. Then, in order to improve the effectiveness of forecasting when ANN was applied to a permeability predicting problem, the design was performed using Cuckoo Optimization Algorithm (COA) algorithm. The validation test result from a new well data demonstrated that the trained COA–LM neural model can efficiently accomplish permeability prediction. Also, the comparison of COA with particle swarm optimization and imperialist competitive algorithms showed the superiority of COA on fast convergence and best optimum solution achievement. [Copyright &y& Elsevier]

Abstract: The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on the law of gravity and mass interactions. It has been proven that this algorithm has good ability to search for the global optimum, but it suffers from slow searching speed in the last iterations. This work proposes a hybrid of Particle Swarm Optimization (PSO) and GSA to resolve the aforementioned problem. In this paper, GSA and PSOGSA are employed as new training methods for Feedforward Neural Networks (FNNs) in order to investigate the efficiencies of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The results are compared with a standard PSO-based learning algorithm for FNNs. The resulting accuracy of FNNs trained with PSO, GSA, and PSOGSA is also investigated. The experimental results show that PSOGSA outperforms both PSO and GSA for training FNNs in terms of converging speed and avoiding local minima. It is also proven that an FNN trained with PSOGSA has better accuracy than one trained with GSA. [Copyright &y& Elsevier]

Abstract: A computation model of fractional-order differentiators based on functional network and the learning Algorithm of functional network using particle swarm optimization was proposed. it provided a new method to compete the numerical calculation of fractional-order differentiators in other fields. the simulation results demonstrate that the method presented in the paper is more efficient and feasible. [Copyright &y& Elsevier]

Abstract: This paper proposes a new metamodeling framework that reduces the computational burden of the structural optimization against the time history loading. In order to achieve this, two strategies are adopted. In the first strategy, a novel metamodel consisting of adaptive neuro-fuzzy inference system (ANFIS), subtractive algorithm (SA), self organizing map (SOM) and a set of radial basis function (RBF) networks is proposed to accurately predict the time history responses of structures. The metamodel proposed is called fuzzy self-organizing radial basis function (FSORBF) networks. In this study, the most influential natural periods on the dynamic behavior of structures are treated as the inputs of the neural networks. In order to find the most influential natural periods from all the involved ones, ANFIS is employed. To train the FSORBF, the input–output samples are classified by a hybrid algorithm consisting of SA and SOM clusterings, and then a RBF network is trained for each cluster by using the data located. In the second strategy, particle swarm optimization (PSO) is employed to find the optimum design. Two building frame examples are presented to illustrate the effectiveness and practicality of the proposed methodology. A plane steel shear frame and a realistic steel space frame are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology. [Copyright &y& Elsevier]

A co-design architecture for the field programmable gate array (FPGA)-based classification system is proposed. In this architecture, a neural network (NN) is trained by the particle swarm optimization algorithm (PSO). The NN is implemented in hardware, and the PSO training is executed by a processor. The ARM processor and the NIOS II processor are used. This architecture maintains the operating speed of the NN. This approach also has the flexibility to change the parameters or even the PSO algorithm without affecting the FPGA part. Three publicly recognized databases (Iris, Balance-scale, Credit approval) are used to evaluate the system. Experimental results showed that the co-design was successfully implemented. In addition, compared to the NIOS II approach, the ARM approach had the advantages regarding the operating speed and the logic utilization. [ABSTRACT FROM AUTHOR]

An important factor in the design of gas injection projects is the minimum miscibility pressure (MMP). A new genetic algorithm (GA)–based correlation and two neural network models (one of them is trained by back propagation [BP] algorithm and another is trained by particle swarm optimization algorithm) have been developed to estimate the CO2–oil MMP. The correlation and models use the following key input parameters: reservoir temperature, molecular weight of C+5, and mole percentage of the volatiles and intermediate components (for the first time, the mole percentages are used as independent variables). Then results are validated against experimental data and finally compared with commonly used correlations reported in the literature. The results show that the neural network model trained by BP algorithm and the correlation that has been developed by GA can be applied effectively and afford high accuracy and dependability for MMP forecasting. [ABSTRACT FROM AUTHOR]