基于生物智能算法的智能控制研究与实践应用

 传统的自动控制方法因为其固定参数等弊端极大限制了控制效果，生物智能算法因为其环境自适应与自学习机制的特性，为突破传统控制方法的瓶颈提供了一种新的思路，并且随着强化学习等机器学习理论与方法的不断完善与发展，生物智能算法的性能也得到了极大的提高。总结了在智能控制中常用的7种生物智能算法，分析了经典的自动控制方法与生物智能算法，尤其是强化学习、深度学习等新型智能算法的结合的应用实例。结合近年来兴起的深度学习，强化学习及类脑智能科学对智能控制的发展现状，以及未来的发展趋势进行展望。强调一种智能辅助控制方法，将智能算法与传统控制方法相结合，为智能控制的研究提供新的思路与实用范例。

An overview and the practical application of the biological intelligence algorithms in intelligence control

The classical control methods have shortcoming,, such as its fixed parameters, with a limited control effect, and the Biological Intelligence Algorithm provides a new way to break the bottleneck of the classical control methods because of its adaptive and learning mechanism. With the improvement of the theory of the reinforcement learning and the deep learning, the performance of the Biological Intelligence Algorithm is greatly improved. This paper reviews seven kinds of intelligent algorithms commonly used in intelligence control, and the application examples of combining the classical automatic control methods and the intelligent algorithms, especially, the reinforcement learning. The development status and the future development trend of the intelligent control based on the reinforcement learning, the deep learning and the Brain-inspired Intelligence Technology in recent years are discussed. The purpose of this paper is to emphasize a new idea of combining the intelligent algorithms with the classical control methods, and provide some new ideas and practical examples for the intelligent control.