以提高径向基函数神经网络(radial basis function neural network,RBFNN)的分类能力为出发点,把衰减半径聚类的思想与误差平方和准则结合起来,提出了RBFNN三阶段学习算法。该算法先利用动态衰减半径聚类确定隐节点的初始结构,再由误差...以提高径向基函数神经网络(radial basis function neural network,RBFNN)的分类能力为出发点,把衰减半径聚类的思想与误差平方和准则结合起来,提出了RBFNN三阶段学习算法。该算法先利用动态衰减半径聚类确定隐节点的初始结构,再由误差平方和准则进行中心点微调,并用类内类间距确定径基宽度,最后采用伪逆法训练隐层与输出层间的连接权重。给出了算法的具体步骤,并通过Iris和WINES数据集的仿真实验,证明该算法确实具有较强的分类能力。展开更多
In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical...In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical system with the quadratic criterion cost function, is employed. In our approach, the model-based optimal control problem is reformulated into the input-output equations. In this way, the Hankel matrix and the observability matrix are constructed. Further, the sum squares of output error is defined. In these point of views, the least squares optimization problem is introduced, so as the differences between the real output and the model output could be calculated. Applying the first-order derivative to the sum squares of output error, the necessary condition is then derived. After some algebraic manipulations, the optimal control law is produced. By substituting this control policy into the input-output equations, the model output is updated iteratively. For illustration, an example of the direct current and alternating current converter problem is studied. As a result, the model output trajectory of the least squares solution is close to the real output with the smallest sum squares of output error. In conclusion, the efficiency and the accuracy of the approach proposed are highly presented.展开更多
文摘以提高径向基函数神经网络(radial basis function neural network,RBFNN)的分类能力为出发点,把衰减半径聚类的思想与误差平方和准则结合起来,提出了RBFNN三阶段学习算法。该算法先利用动态衰减半径聚类确定隐节点的初始结构,再由误差平方和准则进行中心点微调,并用类内类间距确定径基宽度,最后采用伪逆法训练隐层与输出层间的连接权重。给出了算法的具体步骤,并通过Iris和WINES数据集的仿真实验,证明该算法确实具有较强的分类能力。
文摘In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical system with the quadratic criterion cost function, is employed. In our approach, the model-based optimal control problem is reformulated into the input-output equations. In this way, the Hankel matrix and the observability matrix are constructed. Further, the sum squares of output error is defined. In these point of views, the least squares optimization problem is introduced, so as the differences between the real output and the model output could be calculated. Applying the first-order derivative to the sum squares of output error, the necessary condition is then derived. After some algebraic manipulations, the optimal control law is produced. By substituting this control policy into the input-output equations, the model output is updated iteratively. For illustration, an example of the direct current and alternating current converter problem is studied. As a result, the model output trajectory of the least squares solution is close to the real output with the smallest sum squares of output error. In conclusion, the efficiency and the accuracy of the approach proposed are highly presented.
