This paper presents Learning Fuzzy Cognitive Maps (LFCM) as a new paradigm, or approach, for modeling complex adaptive systems (CAS). This technique is the fusion of the advances of the fuzzy logic, formal neural network, and reinforcement learning where they are suitable for modeling systems in artificial life domain of CAS.The FCM structure is similar to a recurrent artificial neural network....

In this paper, a recurrent functional neural fuzzy network (RFNFN) with modified differential evolution (MDE) method is proposed to solve the prediction problems. The proposed RFNFN model adopts a functional link neural network (FLNN) to the consequent part of the fuzzy rules. FLNN uses orthogonal polynomials and linearly independent functions to form a functional expansion. Thus, the consequen...

Based on the electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy neural system design. IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and GA. EM algorithm is a population-based meta-he...

This work deals with the problem of identification of Discrete Possibilistic Dynamic System (DPDS), using the technologies of Genetic Algorithmms (GA). Applying the results from [5-9,11-13,15,16,18-20], the fuzzy recurrent process with possibilistic uncertainty, the source of which is expert knowledge reflections on the states of evolutionary complex extremal system, is constructed. The dynamic...

This paper discusses the application of the newly developed Mahalanobis distance-based ARTMAP (MART) network to the predication of sickle cell anemia patients’ response to Hydroxyurea treatment. Mahalanobis distance-based ARTMAP is a modified version of the Fuzzy ARTMAP networks where the activation and choice function are the same and equal to the Mahalanobis distance between the pattern and t...

In this paper a neurofuzzy control strategy, composed by a neural observer and a fuzzy supervisor, for an anaerobic wastewater treatment process is proposed. The neural observer is based on a recurrent high order neural network (RHONN) which is trained by an extended Kalman filter. The main objective of the observer is to estimate methanogenic biomass, which is employed by a fuzzy supervisor. T...

This talk gives an introduction to a recurrent neural network (RNN) based prosody synthesis method for both Mandarin and Min-Nan text-tospeech (TTS) conversions. The method uses a fourlayer RNN to model the dependency of output prosodic information and input linguistic information. Main advantages of the method are the capability of learning many human’s prosody pronunciation rules automaticall...