Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The w...
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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmonysearchalgorithm (AGOHS) is proposed to overcome the traditional harmonysearchalgorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.
Nowadays, manufacturers rely on trustworthy methods to predict the optimal cutting conditions which result in the best surface roughness with respect to the fact that some constraining functions should not exceed thei...
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Nowadays, manufacturers rely on trustworthy methods to predict the optimal cutting conditions which result in the best surface roughness with respect to the fact that some constraining functions should not exceed their critical values because of current restrictions considering competition found among them in delivering economical and high-quality products to the stringent customers in the shortest time. The present research deals with a modified optimization algorithm of harmonysearch (MHS) coupled with modifiedharmonysearch-based neural networks (MHSNN) to predict the cutting condition in longitudinal turning of X20Cr13 leading to optimum surface roughness. To this end, several experiments were carried out on X20Cr13 stainless steel to attain the required data for training of MHSNN. Feed-forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and the MHS algorithm was used to find the constrained optimum of surface roughness. Furthermore, simple HS algorithm was used to solve the same optimization problem to illustrate the capabilities of the MHS algorithm. The obtained results demonstrate that the MHS algorithm is more effective and authoritative in approaching the global solution compared with the HS algorithm.
We report the improvement of a dynamic modulus model using a modifiedharmonysearch (MHS) algorithm to describe the resistance to rutting and fatigue cracking of asphalt concrete mixtures. The MHS algorithm was refor...
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We report the improvement of a dynamic modulus model using a modifiedharmonysearch (MHS) algorithm to describe the resistance to rutting and fatigue cracking of asphalt concrete mixtures. The MHS algorithm was reformulated to improve the harmonysearch (HS) algorithm by introducing minimum and maximum bandwidths. Using the MHS algorithm, model parameters for lime-modified asphalt concrete mixtures were extracted and a good fit to the dynamic modulus data obtained from laboratory tests was achieved. (C) 2013 Elsevier Ltd. All rights reserved.
Gas?liquid two-phase flow is a typical flow. Its bubble characteristic measurement is of great importance on studying the flow mechanism and guiding the practical fluid mechanical engineering. In this paper, a novel t...
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Gas?liquid two-phase flow is a typical flow. Its bubble characteristic measurement is of great importance on studying the flow mechanism and guiding the practical fluid mechanical engineering. In this paper, a novel three dimensional (3D) multiphase flow imaging device was designed and optimized to measure the transparent object that has an opaque object in the center of the 3D observed area. Its mathematical model was built and the constraints were defined based on the geometrical relationship and design requirements. Based on the original harmonysearch (HS) algorithm, a modified harmony search algorithm (MHS-MC) for improving both the performance on the global optima of the multi-modal problems and the convergence performance of the constrained optimization problems was integrated and applied to optimize the arrangement of the single-camera-multi-mirror device. As a case study, the 3D multiphase flow imaging method was applied in the 3D reconstruction of the cavitation bubble cluster inside a water hydraulic valve. The statistics of the Pareto data show the good performance of the MHS-MC algorithm. And the cavitation experimental results testify the effectiveness of the proposed MHS-MC algorithm. The cavitation bubble cluster can be reconstructed with quite high precision.
This paper proposes an Improved modified harmony search algorithm with constraint handling with application to redundancy allocation problems in reliability engineering. The performance of Improved modifiedharmony Se...
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ISBN:
(纸本)9783662479261;9783662479254
This paper proposes an Improved modified harmony search algorithm with constraint handling with application to redundancy allocation problems in reliability engineering. The performance of Improved modifiedharmonysearch is being compared with that of the original harmonysearch, modified Great Deluge algorithm, Ant Colony Optimization, Improved Non-Equilibrium Simulated Annealing and Simulated Annealing. It is observed that Improved modifiedharmonysearch requires less number of function evaluations compared to others.
Owing to the intermittent nature of the renewable energies employed in smart grids, large frequency fluctuations occur when the load frequency control (LFC) capacity is not enough to compensate for the imbalance of ge...
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Owing to the intermittent nature of the renewable energies employed in smart grids, large frequency fluctuations occur when the load frequency control (LFC) capacity is not enough to compensate for the imbalance of generation and demand. This problem may become intensified when the system is working in an island operation mode. Meanwhile, electric vehicles (EVs) are growing in popularity, being used as dispersed energy storage units instead of small batteries in the systems. Accordingly, the vehicle-to-grid (V2G) power control can be applied to compensate for the inadequate LFC capacity and thereby to improve the frequency stability of smart grids, especially in the island operation mode. On the other hand, large scale and complex power systems encounter many different uncertainties. In order to handle these uncertainties, this study proposes a combination of the general type-2 fuzzy logic sets (GT2FLS) and the modified harmony search algorithm (MHSA) technique, as a novel heuristic algorithm, to adaptively tune the proportional-integral (PI) controller for LFC in islanded MicroGrids (MGs). Although implementing general type-2 fuzzy systems is generally computationally cumbersome, by using a recently introduced plane representation, GT2FLS can be regarded as a combination of several interval type-2 fuzzy logic systems (IT2FLS), each with its own corresponding alpha level and linguistic rules can directly be incorporated into the controller. This paper further presents a new modified optimization algorithm to tune the scaling factors and the membership functions of general type-2 fuzzy PI (GT2FPI) controller and thereby to minimize the frequency deviations of the MG system against load disturbances more effectively. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of the proportional integral derivative (PID), Fuzzy-PID (FPID), and Interval Type II fuzzy based PI (IT2FPI) controllers, which are the most recent method
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