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This paper proposes a new neuro-fuzzy learning machine called extreme learning adaptive neuro-fuzzy inference system (ELANFIS) which can be applied to control of nonlinear systems. The new learning machine combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). The parameters...
We analyze some basic properties of the stock price dynamical model when trend-followers and contrarians interplay with each other. We prove that the price dynamical model has an infinite number of equilibriums, but all these equilibriums are unstable. We demonstrate the short-term predictability of the price volatility and derive the detailed formulas of the Lyapunov exponent as functions of the...
This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis...
An adaptive inferential measurement framework for control and automation systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making inferences related to the presence of anomalies in the surveillance data with the help of statistical, computational and clustering analysis. Moreover, the performance of the ensemble of these tools...
Mixed-binary nonlinear programming (MBNP), which can be used to optimize network structure and network parameters simultaneously, has been seen widely in applications of cyber-physical network systems. However, it is quite challenging to develop efficient algorithms to solve it practically. On the other hand, swarm intelligence based optimization algorithms can simulate the cooperation and interaction...
In order to improve equipment efficiency in terms of performance, energy consumption and degradation for example, the industry has increased the use of control systems as the PID (proportional-integral-derivative) to a new baseline. This structure has few parameters to adjust and it is easy to implement practically. However, there are some requirements often included on multivariable systems that...
A novel approach for optimal robust control of a class of generalized fuzzy dynamical systems is proposed. This is a novel use of fuzzy uncertainty in doing dynamical system control. The system may have nonlinear nominal terms and the other terms with uncertainty, including unknown parameters and input disturbances. The Fuzzy sets theory is creatively employed in presenting the system parameter and...
This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together...
The static output feedback control (SOFC) for Takagi-Sugeno (TS) fuzzy systems is addressed in this paper. Based on Lyapunov theory the proposed methods are formulated as Linear Matrix Inequalities (LMIs). To obtain less conservative conditions the properties of membership functions time-derivative are explored. Wiht this new methodology SOFC with higher H∞ attenuation level can be designed. Moreover,...
We use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. First, we define fuzzy sets to represent the fuzzy terms in the technical trading rules; second, we translate each technical trading heuristic into a group of fuzzy IF-THEN rules; third, we combine the fuzzy IF-THEN rules...
Robotic grasping of a target object without advance knowledge of its three-dimensional model is a challenging problem. Many studies indicate that robot learning from demonstration (LfD) is a promising way to improve grasping performance, but complete automation of the grasping task in unforeseen circumstances remains difficult. As an alternative to LfD, this paper leverages limited human supervision...
The Environmental/Economic Dispatch EED puzzle of power system is actually a classic constrained multi-objective optimization problem in evolutionary optimization category. However, most of its properties have not been researched by its aboriginal Pateto Front. In a meanwhile, the multi-objective evolutionary algorithm based on decomposition(MOEA/D) is a well-known new rising yet powerful method in...
This paper presents a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models and Free Search Differential Evolution (FSDE). We adopt a cascaded evolutionary algorithm approach and problem decomposition to define the model orders and the related model parameters based on higher orders correlation functions. Thus,...
The purpose of this paper is to design a dynamic neural network that can effectively estimate all the states of single input non linear plants. Lyapunov's stability theory along with solution of full form Ricatti equation is used to guarantee that the tracking errors are uniformly bounded. No a priori knowledge on the bounds of weights and errors are required. The nonlinear plant and the dynamic neural...
This paper proposes a tracking controller for a four rotors helicopter robot using dynamic feedback linearization based on piecewise bilinear (PB) models. The approximated model is fully parametric. Input-output (I/O) dynamic feedback linearization is applied to stabilize PB control system. Although the controller is simpler than the conventional I/O feedback linearization controller, the control...
Unlike in the literature, premise variables of the Takagi-Sugeno (TS) fuzzy system is assumed to be not measurable, and an adaptive output feedback control law is designed for the given system. Additionally, the system under investigation is considered to be subjected with both parameteric uncertainty and disturbance. Unlike other control designs, the bound on parameter uncertainty term is relaxed...
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