Dynamic Evolving Fuzzy Neural Networks with ‘m-out-of-n’ Activation Nodes for On-line Adaptive Systems
نویسندگان
چکیده
Abstract. The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, hybrid (supervised/unsupervised), on-line learning, like the EFuNNs. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. At each time moment the output vector of a mEFuNN is calculated based on the m-most activated rule nodes. Two approaches are proposed: (1) using weighted fuzzy rules of Zadeh-Mamdani type; (2) using Takagi-Sugeno fuzzy rules that utilise dynamically changing and adapting values for the inference parameters. It is proved that the mEFuNNs can effectively learn complex temporal sequences in an adaptive
منابع مشابه
DUNEDIN NEW ZEALAND Dynamic Evolving Fuzzy Neural Networks with Ôm-out-of-nÕ Activation Nodes for On-line Adaptive Systems
The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, hybrid (supervised/unsupervised), on-line learning, like the EFuNNs. They can accommodate new input data, including new features, new classes, etc. through local element tu...
متن کاملThe Prediction of Forming Limit Diagram of Low Carbon Steel Sheets Using Adaptive Fuzzy Inference System Identifier
The paper deals with devising the combination of fuzzy inference systems (FIS) and neural networks called the adaptive network fuzzy inference system (ANFIS) to determine the forming limit diagram (FLD). In this paper, FLDs are determined experimentally for two grades of low carbon steel sheets using out-of-plane (dome) formability test. The effect of different parameters such as work hardening...
متن کاملESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS
In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...
متن کاملEvolving Fuzzy Neural Networks: Theory and Applications for On-line Adaptive Prediction, Decision Making and Control
The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created...
متن کاملIdentification of Crack Location and Depth in a Structure by GMDH- type Neural Networks and ANFIS
The Existence of crack in a structure leads to local flexibility and changes the stiffness and dynamic behavior of the structure. The dynamic behavior of the cracked structure depends on the depth and the location of the crack. Hence, the changes in the dynamic behavior in the structure due to the crack can be used for identifying the location and depth of the crack. In this study the first th...
متن کامل