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Habituation is non-associative learning mechanism of biological neurons. This paper studied the simplified description of associative learning mechanism, and based on the classical M-P (McCulloch — Pitts) neuron model, put forward study neurons model with the ability of habituation learning, including habituation neurons. At the same time, in this paper, based on the simplified description of Learning neurons, the mathematical model of habituation neurons is designed, and habituation neurons are applied to deep convolution neural networks. It has been verified by experiment that habituation neurons have typical habituation learning ability, and can optimize the performance of convolution networks.

A class of linear singularly perturbed system and the optimal problem of the upper bound of the perturbed parameter based on the genetic algorithm are considered. Firstly, the problem of the asymptotically stability is studied in the term of Lyapunov stability theory based on the Linear Matrix Inequality (LMI). Then, the standard problem of the upper perturbed parameter to be optimized is presented. Thirdly, the optimization algorithm for the upper bound of the perturbed parameter in the linear singularly perturbed system is given based on the genetic algorithm. Lastly, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed methods.

With the serious pollution of urban industry, sewage treatment has become an urgent problem to be solved. However, there are many problems in the traditional sewage treatment process, resulting in waste of energy, secondary pollution, and so on. In order to overcome the lack of artificial dosing in ozone water treatment system, this paper presents a fuzzy adaptive PID control algorithm for ozone water treatment systems with large time-delay and strong nonlinear characteristic. The Smith predictor is used to compensate for the large time-delay in chemical reaction and detection instrument, which can improve the response speed. The fuzzy adaptive PID controller is designed to deal with the strong nonlinearity. Simulation results are given to verify the effectiveness of the controller.