Application of adaptive genetic neural network algorithm in design of thrust estimator

An adaptive genetic neural network algorithm(AGNNA) was proposed for estimating aeroengine thrust accurately and easily in a full flight envelope.It is based on the combination of genetic algorithm and neural network technology which could make full use of the global convergence property and rapid local searching capability separately.The topology structure and weights(including threshold) of neural network could be directly optimized through adaptive probability genetic operator and local optimizing operator, thereby overcoming the disadvantage of hidden layer nodes of neural network requiring empirical effort and the sensitivity of neural network to its initial weights(including threshold).Then, neural network was applied to "fine-tune" the above-specified weights(including threshold).Finally, the thrust estimator for the full flight envelope was designed with the algorithm.The experimental results show that the thrust estimator has good estimation accuracy and generalization ability.