Recurrent Fuzzy Rules Research Articles

A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters

In this paper, a mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) is proposed for the identification of nonlinear and time-varying systems. The MRIT2NFS uses type-2 fuzzy sets in order to enhance noise tolerance of the system. In the MRIT2NFS, the antecedent part of each recurrent fuzzy rule is defined using interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang type with interval weights. The antecedent part of MRIT2NFS forms a local internal feedback and interaction loop by feeding the rule firing strength of each rule to others including itself. The consequent is a linear combination of exogenous input variables. The learning of MRIT2NFS starts with an empty rule base and all rules are learned online via structure and parameter learning. The structure learning of MRIT2NFS uses online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm to reinforce parameter learning ability. The type-2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by the gradient descent algorithm. After the training, a weight-elimination scheme eliminates feedback connections that do not have much effect on the network behavior. This method can efficiently remove redundant recurrence and interaction weights. Finally, the MRIT2NFS is used for system identification under both noise-free and noisy environments. For this, we consider both time series prediction and nonlinear plant modeling. Compared with type-1 recurrent fuzzy neural networks, simulation results show that our approach produces smaller root-mean-squared errors using the same number of iterations.

Recurrent fuzzy system design using elite-guided continuous ant colony optimization

This paper proposes recurrent fuzzy system design using elite-guided continuous ant colony optimization (ECACO). The designed recurrent fuzzy system is the Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN), in which each fuzzy rule contains feedback loops to handle dynamic system processing problems. The ECACO optimizes all of the free parameters in each recurrent fuzzy rule in a TRFN. Unlike the general ant colony optimization that finds solutions in discrete space, the ECACO finds solutions in a continuous space. The ECACO is a population-based optimization algorithm. New solutions are generated by selection, Gaussian random sampling, and elite-guided movement. To verify the performance of ECACO, three examples of dynamic plant control are simulated using ECACO-optimized TRFNs. The ECACO performance is also compared with other continuous ant colony optimization, particle swarm optimization, and genetic algorithms in these simulations.

TSK-Type Self-Organizing Recurrent-Neural-Fuzzy Control of Linear Microstepping Motor Drives

In this paper, a Takagi-Sugeno-Kang-type self-organizing recurrent-neural-fuzzy network (T-SORNFN) is proposed for the trajectory tracking control of linear microstepping motor (LMSM) drives. Without a priori knowledge, the T-SORNFN is constructed to model the inverse dynamics of a LMSM drive by a set of recurrent fuzzy rules built online through concurrent structure and parameter learning. The fuzzy rules in the T-SORNFN can be either generated or eliminated to obtain a suitable-sized network structure, and a recursive recurrent learning laws of network parameters are derived based on the supervised gradient-descent method to achieve fast-learning converge. Based on the Lyapunov stability approach, the convergence of the T-SORNFN is guaranteed by choosing varied learning rates. Furthermore, an inverse-control architecture that incorporates T-SORNFN and a proportional-derivative controller is used to control the LMSM drive in a changing environment. A recursive least-squares (RLS) algorithm is utilized for online fine-tuning the consequent parameters in T-SORNFN to obtain a more precision model. Simulated and experimental results of a LMSM drive are provided to verify the effectiveness of the proposed T-SORNFN control system, and its superiority is validated in comparison with NFN and RNFN control systems.

A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.

A Neuro-Fuzzy Approach to Optimize Hierarchical Recurrent Fuzzy Systems

To simplify the definition of fuzzy systems or to reduce its complexity hierarchical structures can be used. Thus, more transparent rule bases that are also easier to maintain can be designed. Furthermore, it is sometimes necessary to use time delayed input or to reuse time delayed output from the fuzzy system itself to obtain a rule base that describes the analyzed problem appropriately. This leads to hierarchical recurrent architectures that have increased approximation capabilities since they are able to store information of the past. In this article we present a neuro-fuzzy model that can be used to optimize hierarchical recurrent fuzzy rule bases if training data is available. Furthermore, we present an approach to learn initial rule bases from data using rule templates.