Hankel

HSSVM是一个用超球SVM（Hyper-Sphere Support Vector Machines）模型求解多分类问题的工具包，采用Java语言实现。开发该程序的主要目的，是利用超球SVM求解模型代替传统上借助于解二分类问题的经典SVM模型来求解多分类问题。本文将论述该程序的主要实现细节，包括相关算法及设计原理的描述。(HSSVM is an ultra ball SVM (Hyper-Sphere Support Vector Machines) to solve multi-classification model of tool kit, using Java language. Development of the program' s main purpose is to solve the model by Ball SVM instead of the traditional means of solving two classification problems on the classical SVM model to solve multi-classification problems. This article discusses the main implementation details of the program, including related algorithms and design principles are described.)

说明：  电力市场的最优化分配问题源代码,电气工程同学大作业。(Optimal electricity market distribution source code, electrical engineering students to major operations.)

Intel MIC Platform Software Stack (MPSS).

模板匹配法实现边沿检测，包括点模块、线模块和边沿模块等，文件中包含源图片及其程序运行结果(edge detection)

A Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982. Hopfield nets serve as content-addressable memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but convergence to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum) can occur. Hopfield networks also provide a model for understanding human memory.

求解所输入数据的概率分布，可随意设定所分概率区间个数。(Solving the probability distribution of the input data, can be arbitrarily set the number of sub-probability interval.)

异步电动机的MATLAB仿真，根据异步电机在两相同步旋转坐标系下的数学模型, 利用SIMULINK软件包中的S函数模块和基本模块, 构建出电机的仿真模型。(MATLAB simulation of induction motor，According to the mathematical model of induction motor in the two-phase synchronous rotating coordinate system, the use of SIMULINK package S-function modules and basic modules to build the simulation model of the motor)

加恒定的膜电流,计算0~50ms时间段内 Hodgkin-Huxley模型的积分，并完整地分析和解释所得到的结果(With constant membrane currents, the integral of the Hodgkin-Huxley model in the 0~50ms period is calculated, and the results are fully analyzed and explained)

A 10th order FIR system, excited by a sum of 5 sinusoids. Assume any numeric values for system coefficients and input signal parameters. The output of the system is corrupted by Gaussian observation noise with zero mean and variance 2 σ v (consider 2 σ v = 0.1 and 1). The estimator has access to the input and noise corrupted output of the system. However, the estimator has no knowledge about the system order, its coefficient values and observation noise variance.

This code simulates Distance Vector Routing protocol used for routing in communication networks