作　　者： ;

机构地区： 顺德职业技术学院

出　　处： 《计算机测量与控制》 2010年第3期601-604,610,共5页

摘　　要： 温室环境是一个典型的时变、非线性、强耦合、大滞后及大惯性的复杂被控对象,使用传统方法的控制效果总是不太理想;粒子群算法是一种解决非线性、不可微分问题的优秀算法,具有很强的全局搜索能力,但该算法在进化后期容易出现速度变慢及早熟现象;BP神经网络具有很强的非线性处理能力和逼近能力,但梯度下降的算法本质决定了其具有容易陷入局部最优及初值敏感的缺点;针对两种算法的特性,进行优势互补,结合为综合改进的粒子群BP神经网络(IPSO-BPNN)算法;应用IPSO-BPNN算法对温室内的土壤温度、土壤湿度、空气温度、空气湿度、光照度和CO2浓度等参数进行控制,取得了比较理想的效果。 Greenhouse environment is a typical complex time--varying controlled object of nonlinear, strong coupling, large delay, and large inertia, using traditional control methods always less than ideal results. Particle Swarm Optimization is an excellent algorithm solution for nonlinear, non--differentiable problems. It has strong global search ability, but in the process of looking for the global excellent result, it is easily becoming speed slow and precocious in the later period. BP neural network also has strong nonlinear approximation ability, but its gradient descent algorithm determines that it easy falling into local optimum and sensitive to the initial values. Taking the advantages of the two algorithms, the improved particle swarm optimization and BP neural network （IPSO--BPNN） algorithm is proposed. The IPSO--BPNN algorithm was applied to control the soil temperature, soil moisture, air temperature, air humidity, light intensity, CO2 concentration and other greenhouse parameters, it achieved the desired results.

关 键 词： 改进粒子群算法 神经网络 温室环境控制

领　　域： [自动化与计算机技术] [自动化与计算机技术]