Self-adaptive Evolutionary-programming Based on Three Layer Chromosome and Application

针对模糊辨识中规则数多少难以选择、输入量在各规则中重要程度难以确定以及模糊规则参数难以优化等问题,模拟生物基因之间相互联系的特点,用三阶染色体结构对完全模糊规则基进行描述。根据每代不同个体的适应度信息获得不同个体的变异率,采用自适应进化规划算法,实现模糊规则基各级染色体引导优化,克服了用试凑方法确定参数对辨识精度的消极影响,算法的复杂程度大大减小,为模糊建模提供了一种新颖的方案。通过对两类不同非线性系统的仿真,证明了此方法的有效性。A new method which imitates features of relating in biologys gene is proposed in the paper, and it can solve the problems of fuzzy identify in nonlinear system, such as difficulty in choosing number of rules, weight of input variable in different rules and difficult in optimizing parameters of fuzzy rule bases, etc. In the paper, a three layers chromosome is used to describe perfect rule bases. Different to old methods, an adaptive evolutionary algorithm is used to realize guiding optimum based on the fitness information of different individual. Moreover, the whole chromosome is optimized simultaneously. The new method remedies the bad effect of compromise proposal in identifying of old method and the given algorithm is simple. The effectiveness of the given method is demonstrated by simulations of different nonlinear systems.安徽省高等学校青年教师资助计划项目(2002jq148

An Improved PSO Method Based on Adaptive Cognitive Domain

为提高粒子群算法的收敛性能,提出一种自适应粒子认知域方法.在粒子位置的更新方法中,粒子运动到当前的最好位置由计算得到的最好位置为中心,粒子的认知方向为导向来确定.利用线性惯性下降权重来实现粒子的优化.为验证该方法的有效性,将此方法应用于3种不同的粒子群方法,分别是固定权重粒子群方法、线性下降权重粒子群方法及阶梯形群体粒子群算法.实验结果表明此方法是较有效的.To improve the convergent performance of particle swarm optimization(PSO),an adaptive cognitive domain particle swarm optimization(ACDPSO)method is proposed.In the updating equations of particles,the current best position,which the particle achieves,is determined by the center of the best calculated position and the cognizant direction of the particle.Linear decreasing inertia weight is used to optimize particles.Three different PSOs,particle swarm with constant weight(CWPSO),linear decreasing inertia weight PSO(LDWPSO)and Ladder PSO(LPSO),are combined with the proposed method to test the performance of the proposed method,and the results indicate that the proposed method is effective.教育部科学技术研究重点项目(No.209057);安徽省自然科学基金项目(No.090412070);高等学校省级优秀青年人才基金项目(No.2009SQRZ088ZD);高等学校省级自然科学研究项目(No.KJ2009B062)资

Predictive Control Algorithm Based on High-Order Fuzzy Internal Model and Its Applications in Intermittent Coal Gasification Reactors

针对大时滞系统,提出一种高阶模糊内模预测控制算法。通过引入智能因子,被控对象的高阶模糊内部模型可实时预测对象输出,在线修正模型失配,从而克服时滞因素的不利影响。将算法应用于化工间歇造气炉集散控制系统中,现场运行结果验证了算法的实用性。A predictive control algorithm based on high--order fuzzy internal model, which aimed at systems with dead time, is proposed. By introducing intelligent factor, the high--order fuzzy internal model of controlled plant can forecast output of process in time and modify model uncertainty so as to overcome the disadvantages of dead time. Running results certify its practical values in apply-- ing the algorithm to the distributed control systems of coal gasification reactors.福建省青年科技人才创新资助项目(No.2004J020

Design of Parameter-nonlinear Fuzzy-PID Controller Based on Complex-valued Encoding Evolutionary-programming

针对大时滞、非线性的复杂的工业过程,传统的固定参数PID控制器很难满足实际要求,为此,设计一种无量化因子的参数非线性模糊PID控制器.为优化初始的控制器参数,利用复数编码方法来设计进化规划,即个体的信息由染色体的模和幅角共同确定.这种双倍体形式的个体,相对于二进制编码方法而言,大大拓展了群体的信息量,加快了算法的收敛速度.仿真实验表明:该方法对非线性系统具有较快的收敛速度和良好的响应特性.A parameter-nonlinear fuzzy-PID controller without numerical factors,which aimed at time-delay and nonlinear complicated industrial process,is designed,because the PID controller of conventional fixed-parameters couldn't satisfied with practical demand.In order to optimize initial parameters of controller,the complex-valued encoding method is used to design the evolutionary-programming,namely,individual information are determined by the modules and angles of chromosomes.The form of double individual makes group information more rich and rapids convergent speed compared to decimal-valued encoding method.The simulation experiment shows that the method possesses rapid convergent speed and good response performance.福建省青年科技人才创新项目(2004J020)资

Application of Evolutionary Programming and Fuzzy Algorithm in PID Control

针对非线性控制系统,利用进化规划模糊算法,对PID控制器规则基的参数进行离线优化,优化后的模糊控制规则可对系统实现实时控制。该控制算法无需任何先验知识和量化因子,具有很强的数据挖掘能力,且模糊规则基的寻优速度较快。通过对非线性系统进行仿真,验证了该算法的有效性,与传统固定参数PID控制方法及遗传算法整定参数PID控制方法相比,明显地提高了系统的稳定性和动态性能。An evolutionary programming and fuzzy algorithm is proposed for nonlinear complicated systems.Evolutionary programming is used in offline training to optimize controller's rule base.Then the optimized base is implemented in online control.The novel method doesn't need any numerical factor and prior experience while possessing strong data-mining ability and rapid optimized speed of fuzzy rule base.Finally,compared with the traditional PID control algorithm and the PID controller with the genetic algorithm based parameter adjustment the simulation result shows that the proposed method has made a great step in the stability and dynamic performance of the given system.厦门大学985二期信息创新平台资助项

Adaptive Control Algorithm Based on Linear Fuzzy Internal Models

针对非线性对象,提出一种线性化模糊内模自适应控制算法。该算法以一组模糊规则作为非线性对象内部模型,一条模糊规则表示一个局部线性系统;根据对象输入与输出测量值,利用TSK建模方法在线辨识局部模糊内部模型;同时依据辨识模型设计局部H2最优模糊控制规则,所有规则构成H2最优模糊控制器。仿真实验显示:该算法适用于非线性对象的控制,具有较好的鲁棒性和抗干扰能力。A control algorithm which aimed at nonlinear plant is proposed. The control scheme adopts a group of fuzzy rule sets as internal model of the nonlinear plant, where a fuzzy rule set represents a local linear system. This algorithm utilizes TSK modeling scheme to identify fuzzy internal model on line by using the input and output measurement of the plant; at the same time it designs local fuzzy control rule set of linear quadratic optimal ( H 2 ) based on the identified model and all fuzzy control sets consist of H 2 fuzzy controller. It is shown in the simulations that the proposed control method is suitable for nonlinear plant and possesses satisfactory robust performance and disturbance rejection ability