Radical Basis Function Neural Network Model Predict The Performance Of Evaporative Cooling Air Conditioning Filler

With the economic development and people’s living standards greatly improved,air conditioning has become an important part of modern human life. Evaporativecooling air conditioning as a green energy saving air conditioning, its application filedis more and more widely. Filler as the core component of the evaporative cooling airconditioning, its performance will directly affect the efficiency of the heat exchanger,packing performance prediction has become the focus of the research of evaporativecooling air conditioning. Due to the performance of the packing not only with packingmaterial itself, but also related to air condition, air velocity, water spray density and soon many factors. so establish a fast running and high precision filler performanceprediction model for evaporative cooling air conditioning research has theoreticalsignificance and practical value.Radial basis function neural network as an effective tool to solve nonlinearproblems, it can be based on the actual establishment of the network topology. Has thefeatures of simple structure, fast learning, not easy to fall into the local minimumvalue, can approximate any nonlinear continuous function etc In this paper, based onin-depth analysis of the key technical problems faced by the packing performanceprediction and compared various fillers performance prediction method, explore usethe RBF neural network to predict the filler performance. RBF neural networkprediction model by learning of historical data, find out the inherent law of fillerperformance and influencing factors, and is stored in the network, used to predict theperformance of the filler.The paper first introduces the filler performance prediction of existing methods,analyze their advantages and disadvantages. then discussed the structure of the RBFneural network, principles, characteristics, etc. Put forward the filler performanceprediction model based on RBF neural network. Finally, established a predictionmodel by MATLAB software, then training and simulation model, verified predictionmodel has a good accuracy. thus proving the feasibility and accuracy of the RBF neural network to forecast filler performance of evaporative cooling air conditioning.Research shows that: based on the performance of RBF neural networkprediction evaporative cooling air conditioning packing is feasible, network modeltraining is fast; In addition to a handful of predicted value and measured value have alarge deviation, most of the dry bulb temperature forecast value and the moisturecontent prediction, all the corresponding measured value deviation is small, theaverage relative error is less than1%, the mean square error is less than0.1, withinthe permissible error scope.With the help of artificial neural network predictive ability of nonlineardynamical systems, the establishment of a filler performance prediction model basedon RBF neural network evaporative cooling air conditioning, evaporative cooling airconditioning running parameters of filler performance, evaporative cooling airconditioning system the performance of research, design and control optimization toprovide a reliable reference. The issues involved in automatic control science, HVAC,science, computer science and other disciplines, filler for the research of evaporativecooling air conditioning, heat and mass transfer performance provides a new idea, andthe results of their research on the direct evaporative cooling air conditioning directsegment filler performance has a very important practical significance.