Title :
Simple evolving connectionist systems and experiments on isolated phoneme recognition
Author :
Watts, Michael ; Kasabov, Nik
Author_Institution :
Dept. of Inf. Sci., Otago Univ., Dunedin, New Zealand
Abstract :
Evolving connectionist systems (ECoS) are systems that evolve their structure through online, adaptive learning from incoming data. This paradigm complements the paradigm of evolutionary computation based on population based search and optimisation of individual systems through generations of populations. The paper presents the theory and architecture of a simple evolving system called SECoS that evolves through one pass learning from incoming data. A case study of multi-modular SECoS systems evolved from a database of New Zealand English phonemes is used as an illustration of the method
Keywords :
adaptive systems; evolutionary computation; learning (artificial intelligence); neural nets; search problems; speech recognition; word processing; ECoS; New Zealand English phoneme database; case study; evolutionary computation; evolving system; incoming data; isolated phoneme recognition; multi-modular SECoS systems; one pass learning; online adaptive learning; population based search; simple evolving connectionist systems; Computer architecture; Databases; Evolutionary computation; Fuzzy neural networks; Genetic algorithms; Information retrieval; Information science; Optimization methods; Radio access networks; Telephony;
Conference_Titel :
Combinations of Evolutionary Computation and Neural Networks, 2000 IEEE Symposium on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-6572-0
DOI :
10.1109/ECNN.2000.886239
