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微处理器开发 FIC8120 方案 Multi-ICE
FIC8120 方案 Multi-ICE
数学计算 This a simple genetic algorithm implementation where the evaluation function takes positive values o
This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
并行计算 A Function Optimizer using Simple Genetic Algorithm developed from the Pascal SGA code
A Function Optimizer using Simple Genetic Algorithm developed from the Pascal SGA code
matlab例程 it is a simulation about ML synchronization algorithm in OFDM systems,you can slao see a function pi
it is a simulation about ML synchronization algorithm in OFDM systems,you can slao see a function picture in its output,that s useful for a beginner
人工智能/神经网络 Multi-Agent采购系统的遗传优化研究
Multi-Agent采购系统的遗传优化研究
金融证券系统 // chebysheve outlier detection // this function is used to detect the abnormal value among a set o
// chebysheve outlier detection
// this function is used to detect the abnormal value among a set of data
// input:
// delta: a set of data
// flag: discribe which data is already known as outlier
// p: restrict level
// output:
// double[] door : byyond which the data may be considered as a outlie ...
单片机开发 I often need a simple function generator. Just to generate a certain frequency. After all the years
I often need a simple function generator. Just to generate a certain frequency. After all the years I ve worked with electronics, I still haven t got me one. Even though I need it now and then, I just couldn t seem to justify the cost of one.
So, standard solution - build one yourself.
I designed a ...
人工智能/神经网络 第13讲 智能Agent及多Agent系统 Chapter 13 Intelligent Agent & Multi-Agent Systems 徐从富 浙江大学人工智能研究所
第13讲 智能Agent及多Agent系统
Chapter 13 Intelligent Agent & Multi-Agent Systems
徐从富
浙江大学人工智能研究所
通讯/手机编程 capture frames in mobile 6.0 you can edit raw data with callback function
capture frames in mobile 6.0
you can edit raw data with callback function
人工智能/神经网络 function [U,center,result,w,obj_fcn]= fenlei(data) [data_n,in_n] = size(data) m= 2 % Exponent fo
function [U,center,result,w,obj_fcn]= fenlei(data)
[data_n,in_n] = size(data)
m= 2 % Exponent for U
max_iter = 100 % Max. iteration
min_impro =1e-5 % Min. improvement
c=3
[center, U, obj_fcn] = fcm(data, c)
for i=1:max_iter
if F(U)>0.98
break
else
w_new=eye(in_n,in_n)
center1=sum(cent ...