Unit coordinated control in thermal power plants is a system which is complex,nonlinear and is difficulty to establish accurate model, So it is hard to make system gain optimum running effect with conventional control...
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Unit coordinated control in thermal power plants is a system which is complex,nonlinear and is difficulty to establish accurate model, So it is hard to make system gain optimum running effect with conventional control strategy. PSO-RBF neural network is used to identify the mathematical model of coordinated control system and acts as a predictive model in generalized predictive control strategy, which is to achieves predictive control with online rolling optimization and real time feedback revision. Simulation results show that it has a strong robustness when the load condition changes,or big lag affects.
This paper presents a way of combining BP(Back Propagation) neural network and an improved PSO(particleswarmoptimization) algorithm to predict the earthquake *** is known that the BP neural network and the normal PS...
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ISBN:
(纸本)9781467397155
This paper presents a way of combining BP(Back Propagation) neural network and an improved PSO(particleswarmoptimization) algorithm to predict the earthquake *** is known that the BP neural network and the normal PSO-BP neural network have some defeats,such as the slow convergence rate,easily falling into local minimum *** improving the properties of PSO,some proposed the linear decreasing inertia weight ***,this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal *** with the linear decreasing strategy,the inertia weight in our nonlinear method has a faster declining speed in the early iteration,which can enhance the searching *** the late iteration,the inertia weight has a slower declining speed to avoid trapping in local minimum *** we apply the improved PSO to optimize the parameters of BP neural *** the end,the improved PSO-BP neural network is applied to earthquake *** simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.
In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detect...
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In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detected weak signal based on the Duffing oscillator. For this purpose, the variance of the Duffing oscillator's output is exploited, which has the property of multi-extremum single-maximum (MESM) distribution with the frequency of the periodic signal. The impact of signal's phase on the MESM distribution is discussed. When the signal's phase is known, the frequency of the signal can be directly identified as that with the maximal variance, which leads to a nonlinear optimization problem that can be solved by a particleswarmoptimization (PSO) algorithm. When the phase is unknown, the pi/2-phase-shift method is to be exploited integrated with a PSO algorithm. It is shown that the frequency can be precisely and efficiently identified by this method, whose effectiveness is verified by simulation results in Matlab.
Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparat...
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Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by bla...
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Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particleswarmoptimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. (c) 2014 Elsevier Ltd. All rights reserved.
With the increasing requirements of the hydrodynamic performance of the propeller, the optimization design of propeller has been gradually taken by people. With the DTRC series propellers as master model, this paper u...
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With the increasing requirements of the hydrodynamic performance of the propeller, the optimization design of propeller has been gradually taken by people. With the DTRC series propellers as master model, this paper uses the theoretical prediction program based on surface panel method and combines with particle swarm optimization algorithm to study the optimization of propeller pitch (the other parameter is the same as the original propeller). In the optimization process, there are two different kinds of pitch expression (linear superposition method and Bezier function method) to fit radial distribution of pitch. With open water efficiency as the goal, the propeller is optimized and then discusses the influence of skew on open water efficiency. The result shows that Bezier method fits pitch curve more smoothly compared with hick-henne method, and that under the condition of meeting the thrust coefficient, the optimized propeller of Bezier method has higher open water efficiency.
As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm ope...
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As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on particle Swam optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model. (C) 2013 Elsevier B.V. All rights reserved.
Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for s...
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Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results: This paper presents an effective search strategy for a particleswarmoptimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particleswarmoptimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study.
Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented...
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ISBN:
(纸本)9780769539010
Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. We use rough set to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.
Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particleswarmoptimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the divers...
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ISBN:
(纸本)9781424438181
Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particleswarmoptimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the diversity of population become low, the individuals will be dispersed and turn into diffusion phases, while if the diversity of population get high, the individuals carry out the attraction phases. It is indicated that MDPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO by applying MDPSO to portfolio problem and comparing with SPSO and other algorithms.
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