Design Of Industry Steady-state Optimization Based On ACO

The performance requirements to control systems have been increased constantly with the fact that industry process run increasingly to large-scale and automation. Technology of steady-state optimization can find out or keep the equipment parameters or handicraft variable which maked the industry process to work in the best condition according to some target of control system.It is a control technique of little devotion and quick effect.How to set up accurate system models and find out a kind of effective optimization algorithm are the two critical problems of steady-state optimization.Artificial neural network was used to model intelligently focus on the characteristics of multivariate,nonlinearity and inaccuracy.Artificial neural network not only can approach any nonlinearity,but also has large-scaled parallel process,knowledge distributed store,strong self-learning and fault-tolerance well and so on.Where,multilayer feedforward neural network can approximate any continuous function at arbitrary precision . Radial Rasis Function (RBF) Neural Network is a typical form.RBF Neural Network is a partial neural network, its structure and training is simple, but learning convergence is fast.In the thesis RBF based on Resource Optimizing Networks (RON) was used to model,to realize simplify of network structure,and to guarantee the generalization capability of RBF.We verified it by experiments.Ant Colony Optimization(ACO) was used to look for optimizing parameters in the thesis.In order to overcome the shortcoming that ACO was easy to be in local optimum and its convergence was slow,ACO was improved.The speed of looking for optimizing parameters was increased and overall was improved by changing volatilization factor and intensity of pheromone self-adaptively.Effectiveness of the algorithm was verified by simulation on the route of 31 cities.In the thesis we took break up cell of CHP as the object.We have determined the controllable parameters and the target of optimization specifically by carrying out large amount of scene data collection and analyzing via the acquaintance of the productive technology.We used RBF based on Resource Optimizing Networks and improved ACO to model and optimize,and have verified the validaty of the algorithm by experiments.