Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during...Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.展开更多
Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this r...Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.展开更多
This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex ...This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.展开更多
准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和...准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型.首先第一阶段先用LSTM进行燃气负荷初步预测,然后计算出燃气负荷残差值,第二阶段先用BPNN去预测残差值,然后用Adam自适应学习率算法在学习过程中自动调节LSTM-BPNN残差模型的学习率,加快拟合速度,接着用模糊编码遗传算法去优化BPNN的初始权重和阈值,以便寻找到全局最优解.最后把两阶段的预测值和作为最终的燃气负荷预测值.通过对比实验得出,本文模型比单模型,原始两阶段预测模型得到了更高的预测准确率.展开更多
A genetic learning algorithm based fuzzy neural network was proposed for noisy image restoration, which can adaptively find and extract the fuzzy rules contained in noise. It can efficiently remove image noise and pre...A genetic learning algorithm based fuzzy neural network was proposed for noisy image restoration, which can adaptively find and extract the fuzzy rules contained in noise. It can efficiently remove image noise and preserve the detail image information as much as possible. The experimental results show that the proposed approach is able to performa far better than conventional noise removing techniques.展开更多
Obtaining the average delay and selecting a route in a communication networkare multi-constrained nonlinear optimization problems . In this paper, based on the immune geneticalgorithm, a new fuzzy self-adaptive mutati...Obtaining the average delay and selecting a route in a communication networkare multi-constrained nonlinear optimization problems . In this paper, based on the immune geneticalgorithm, a new fuzzy self-adaptive mutation operator and a new upside-down code operator areproposed. This improved IGA is further successfully applied to solve optimal problems of computercommunication nets.展开更多
文摘Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.
文摘Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.
文摘This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.
文摘准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型.首先第一阶段先用LSTM进行燃气负荷初步预测,然后计算出燃气负荷残差值,第二阶段先用BPNN去预测残差值,然后用Adam自适应学习率算法在学习过程中自动调节LSTM-BPNN残差模型的学习率,加快拟合速度,接着用模糊编码遗传算法去优化BPNN的初始权重和阈值,以便寻找到全局最优解.最后把两阶段的预测值和作为最终的燃气负荷预测值.通过对比实验得出,本文模型比单模型,原始两阶段预测模型得到了更高的预测准确率.
基金National Natural Science Foundation ofChina!( 69772 0 0 2 )
文摘A genetic learning algorithm based fuzzy neural network was proposed for noisy image restoration, which can adaptively find and extract the fuzzy rules contained in noise. It can efficiently remove image noise and preserve the detail image information as much as possible. The experimental results show that the proposed approach is able to performa far better than conventional noise removing techniques.
文摘Obtaining the average delay and selecting a route in a communication networkare multi-constrained nonlinear optimization problems . In this paper, based on the immune geneticalgorithm, a new fuzzy self-adaptive mutation operator and a new upside-down code operator areproposed. This improved IGA is further successfully applied to solve optimal problems of computercommunication nets.
