In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a rob...

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...

We show that a reinforcement learning method, adaptive critic based approximate dynamic programming, can be used to create fuzzy policy managers for adaptive control of a logistic system. Two different architectures are used for the policy manager, a feed forward neural network, and a fuzzy rule base. For both architectures, policy managers are trained that outperform LP and GA derived fixed po...

A method for building the rule-base of a fuzzy controller, using the iterative learning and adaptive neural fuzzy training is tested in practical conditions. This method aims to engage intelligent features to controller design procedure, by implying concepts and techniques from artificial intelligence as learning or adapting. An iterative self-learning algorithm is used to gather useful and tru...

A supervisory hybrid system may be modeled from two levels: logic level (upper) and continuous level (lower). In this paper, adaptive fuzzy Petri nets and neural networks are combined together for supervisory hybrid system modeling. Adaptive Fuzzy Petri Net is adopted to model the supervisory logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples ar...

Decision making in a complex, dynamically changing environment is a difficult task that requires new techniques of computational intelligence for building adaptive, hybrid intelligent decision support systems (HIDSS). Here, a new approach is proposed based on evolving agents in a dynamic environment. Neural network and rule-based agents are evolved from incoming data and expert knowledge if a d...

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks fuzzy logic offers numerous advantages, creating robust adaptive system capable handling complex dynamics uncertainties. This learns from its environment adjusts behavior, making it effective challenging situations. Additionally, i...

Purpose Respiratory motion prediction is a chaotic time series prediction problem. In this study, respiratory motion predictability from 12 traces from breast cancer patients is examined by using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Interval Type-2 Non Singleton Fuzzy System (IT2NSFLS). Methods Free breathing data curves were obtained from Real Time Position Management system (RPM ...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...