A new self-growing neural network (NN) based on hybrid neurons is proposed for high accuracy of nonlinear identification. Hybrid neural network achieved the characteristics of rapid growth

accurate results and less neurons through hybrid hidden layer consist of summation units and multiplication units. A variant of quantum particle swarm optimizer called Guiding Quantum Particle Swarm Optimizer incorporating Immune algorithm (GQPSOI) to guide the growth of the neural network structure and weights updation. Through the fuel cell modeling and comparative analysis

the proposed method was proved to be effectiveness and of good application prospect.