Model-reference adaptive control with neurofuzzy methodology is derived in this paper. Associate memory network(AMN) is investigated in detail to be the possible implementation as the direct self-tuning nonlinear controller. The essence of the neurofuzzy controller has been discussed and the local stability of the system is reached. The performance of the model-reference adaptive neurofuzzy con...

This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network (NFN-MK), an hybrid computational model that combines fuzzy system techniques and artificial neural networks. Its main task consists in the automatic generation of membership functions, in particular, triangle forms, aiming a dynamic modeling of a system. The model is tested by simulating real systems, here represented by a ...

Owing to the evolution of the smart grid, the emergence of hybrid power systems (HPSs), and the proliferation of plug-in-hybrid electric vehicles, the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. In this paper, an incremental conductance (IC) based Her...

This paper addresses the problem of rate control for Available Bit Rate (ABR) service class in Asynchronous Transfer Mode (ATM) networks. An adaptive neurofuzzy mechanism based on Adaptive Network Fuzzy Inference System (ANFIS) for allocating rates in ABR service has been proposed and compared with the fuzzy technique called as Fuzzy Explicit Rate Marking (FERM). To achieve this, a neurofuzzy A...

This paper reports on the design and implementation of a neurofuzzy system for modelling and controlling drilling processes in an Ethernet-based application. The neurofuzzy system in question is an Adaptive Network based Fuzzy Inference System (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. The mai...

This research work proposes a multi-input multi-output (MIMO) online adaptive feedback linearization NeuroFuzzy control (AFLNFC) scheme to improve the damping of low frequency oscillations (LFOs) in an AC/DC power system. Optimized NeuroFuzzy identification architecture online captures the oscillatory dynamics of the power system through wide area measurement system (WAMS)-based measured speed ...

In this work we develop an input-output recurrent neurofuzzy network in discretetime for identification and control of nonlinear systems. The structure is linear in the consequent parameters and nonlinear in the antecedent ones. The training of the antecedent parameters is achieved by linearizing them around a suboptimal value, assuming that the only known data are input-output signals obtained...

Fuzzy systems, neural networks and its combination in neuro-fuzzy systems are already well established in data analysis and system control. Especially, neurofuzzy systems are well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a-priori knowledge into the process of data analysis. However, its rec...

In this paper, a neurofuzzy adaptive control framework for discrete-time systems based on kernel smoothing regression is developed. Kernel regression is a nonparametric statistics technique used to determine a regression model where no model assumption has been done. Due to similarity with fuzzy systems, kernel smoothing is used to obtain knowledge about the structure of the fuzzy system and th...

Modelling has become an invaluable tool in many areas of research, particularly in the control community where it is termed system identification. System identification is the process of identifying a model of an unknown process, for the purpose of predicting and/or gaining an insight into the behaviour of the process. Due to the inherent complexity of many real processes (i.e multivariate, non...