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In this paper, an orthogonal functions neural network is used to achieve the control of nonlinear systems. The adaptive controller is constructed by using Chebyshev orthogonal polynomials neural network, which has advantages such as simple structure and fast convergence speed. The adaptive learning law of orthogonal neural network is derived to guarantee that the adaptive weight errors and tracking errors are bound by using Lyapunov stability theory. Simulation results are given for a two-link robot in the end of the paper, and the control scheme is validated.

详细的说明了如何使用该数学软件，很容易学！(Detailed instructions on how to use the mathematical software, it is easy to learn )

这是一个计算一维时间序列盒维数的计算程序，用matlab语言编写，希望对大家有帮助。(This is a calculated one-dimensional time series box dimension of the calculation procedures, using Matlab language. We want to help.)

ART重建算法，该算法可用于CT图像重建或EST图像重建-Matlab ART reconstruction algorithm, which can be used for CT image reconstruction or image reconstruction EST(matlab reconstruction algorithm, which can be used for CT image reconstruction or image reconstruction EST)

Matlab program files mitra 3 edition

this is the linearized model of the power system

this .m file describes how to apply filters on Images ( sobel , Robert ... etc )..

在一定区域内画圆，各个圆以相同的速度膨胀，直至填满该区域。(Draw circles and the circles can be full in the rectancle )

将图像分为16*16的分块进行计算,稀疏矩阵采用DCT矩阵，观测矩阵采用高斯随机矩阵，重构采用OMP算法,对256*256大小的8bit灰度lena图像进行仿真( )

The paper deals with the fuzzy control of a wastewater treatment process in which the organic substances are removed. The process is treated as a multivariable process having as control inputs the dilution rate, the aeration rate and the recycling rate and as output variables the dissolved oxygen and the substrate concentrations in the effluent. The process sensitivities with respect to the control inputs in the case of indirect control through the control of dissolved oxygen concentration and of the direct control of substrate were studied. In all cases fuzzy controllers were used. It was also shown that fuzzy control provides good results in the presence of disturbances and parametric uncertainties of the model.