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找到约 5,352 项符合「Learning」的源代码

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热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/246998/12693696

m chap4_1.m

%Single Neural Adaptive PID Controller clear all; close all; x=[0,0,0]'; xiteP=0.40; xiteI=0.35; xiteD=0.40; %Initilizing kp,ki and kd wkp_1=0.10; wki_1=0.10; wkd_1=0.10; %wkp_1=rand;
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www.eeworm.com/read/143975/12826081

m chap4_1.m

%Single Neural Adaptive PID Controller clear all; close all; x=[0,0,0]'; xiteP=0.40; xiteI=0.35; xiteD=0.40; %Initilizing kp,ki and kd wkp_1=0.10; wki_1=0.10; wkd_1=0.10; %wkp_1=rand;
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www.eeworm.com/read/143733/12847990

readme

Backpropagation learning: bp_innerloop The backpropagation learning algorithm, used in each of the demos below. XOR Demo: bpxor.m Learning the XOR function. XorPats.m Input patterns for
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www.eeworm.com/read/143706/12849510

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/140851/13058955

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampli
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www.eeworm.com/read/140090/13109639

ini ltest.ini

[count] count=4 [Master] master=0 [test trip] 1trip=PowerOn 2trip=Message 2Msg=D:\SmartJTestSystem\message\LTestkeyMsg_4.txt 3trip=Learning Trip 4trip=PowerOff
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www.eeworm.com/read/326325/13147022

m single neural adaptive pid controller.m

%Single Neural Adaptive PID Controller clear all; close all; x=[0,0,0]'; xiteP=0.40; xiteI=0.35; xiteD=0.40; %Initilizing kp,ki and kd wkp_1=0.10; wki_1=0.10; wkd_1=0.10; %wkp_1=rand;
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www.eeworm.com/read/139562/13148859

m chap4_1.m

%Single Neural Adaptive PID Controller clear all; close all; x=[0,0,0]'; xiteP=0.40; xiteI=0.35; xiteD=0.40; %Initilizing kp,ki and kd wkp_1=0.10; wki_1=0.10; wkd_1=0.10; %wkp_1=rand;
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www.eeworm.com/read/138798/13211959

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampli
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www.eeworm.com/read/323831/13314810

h cdynamicprogramming.h

// Copyright (C) 2003 // Gerhard Neumann (gerhard@igi.tu-graz.ac.at) // // This file is part of RL Toolbox. // http://www.igi.tugraz.at/ril_toolbox // // All rights reserved.
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