Machine rule induction was examined on a difficult categorization problem by applying a Hollandstyle classifier system to a complex letter recognition task. A set of 20,000 unique letter images was generated by randomly distorting pixel images of the 26 uppercase letters from 20 different commercial fonts. The parent fonts represented a full range of character types including script, italic, serif, and Gothic. The features of each of the 20,000 characters were summarized in terms of 16 primitive numerical attributes. Our research focused on machine induction techniques for generating IF-THEN classifiers in which the IF part was a list of values for each of the 16 attributes and the THEN part was the correct category, i.e., one of the 26 letters of the alphabet. We examined the effects of different procedures for encoding attributes, deriving new rules, and apportioning credit among the rules. Binary and Gray-code attribute encodings that required exact matches for rule activation were compared with integer representations that employed fuzzy matching for rule activation. Random and genetic methods for rule creation were compared with instance-based generalization. The strength/specificity method for credit apportionment was compared with a procedure we call "accuracy/utility."

This solutions manual provides solutions to the problems contained in the first edition of our book STATISTICAL AND ADAPTIVE SIGNAL PROCESSING. The solutions have been prepared and written by David Marden and ourselves. We have attempted to provide very detailed solutions to the problems with notation consistent with that used in the book. Where applicable, we have also given a printout of Matlab code and figures for the problem solution.

控制理论和最优滤波器设计的经典教材，有很强的专业性，适合控制理论和信号处理方面的进阶。

adaptive filter theory 4th英文原版扫描，ocr处理可搜索，带书签

介绍QRD-RLS 自适应滤波 的经典书籍

经典beamforming、自适应滤波教材matlab源代码。 Paulo S.R. Diniz编著的自适应滤波第四版（Adaptive Filtering_Algorithms and Practical Implementation 4th），源代码——Nonlinear_Adaptive_Filters

本代码为自适应局部迭代滤波（ALIF）处理振动信号，内有可运行的示例（demo文件），添加了相关注释，matlab2018可以直接运行，其他版本请自行尝试。ALIF是2016年Antonio Cicone提出的一种自适应信号处理方法，原作者论文已附在压缩包中，代码中的示例效果并不代表最佳效果，只作为运行展示使用。

当代最经典的自适应神经网络控制书 由IEEE Fellow Shuzhi Sam Ge 编写

自适应霍夫曼编码的C++版本简单实现 class AdaptiveTree { public: AdaptiveTree(int rootNum); AdaptiveTree(int rootNum, string str); void swap(int first, int second); // swap two nodes of the tree void initalCode(); // initializing the data string char2code(unsigned char letter); // locate the character in the tree with its corresponding binary string and return the string string char2binary(unsigned char letter); // translating the character to the 8-bit binary string unsigned char binary2char(string bin); // translating the binary string: bin to the corresponding character int spawn(unsigned char letter); // add a new character to the original tree void updateTree(unsigned char newchar); // update the tree int highestInBlock(int count); // return the highest node to be exchanged void setString(string str); // string decodingStr() const; void encoding(); string decoding(); unsigned char code2char(string bincode); static int size(); string binStr() const; // return the binary string of string: tempString private: void run(); int findchar(unsigned char letter ); // locate the letter in the tree string tempString; //temp string to be encoded needed to be stored here string deStr;// used for storing the decoding string string bin; // used for storing the result of encoding process /* Adaptive Tree data members */ HuffmanTree *tree; int root; /* Adaptive Tree constants */ static int ALPH_SIZE; // size of the alphabet static unsigned char none; // not a unsigned character static unsigned char NYT; // Not Yet transmitted code };

自适应共振理论ART2(Adaptive Resonance Theory)的python实现