The neuro-fuzzy SoftwarE for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The SoftwarE trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂，據高手說，非常有用。
Understanding of SoftwarE development methodologies and concepts, experience with full product lifecycle from scope to customer release
5. Strong design decomposition skills, analysis and testing
6. Proactive approach to problem solving
7. Strong communication skills are paramount as is drive and determination, and stron
This LDPC SoftwarE is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My SoftwarE is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
