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A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

ARO: A new model free optimization algorithm for real time applications inspired by the asexual reproduction

This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA.     

COMPARISON OF FLIGHT CONTROL SYSTEM DESIGN METHODS IN LANDING

Intelligent control theory usually involves the subjects of neural control and fuzzy logic control. The great potential of intelligent control in control designs has recently been realized in the literature. In this survey paper, we attempt to employ this subject and provide the reader with an overview of related topics, such as conventional, fuzzy logic‐based, neural net‐based adaptive control techniques. Practical control schemes realistically applicable in the area of control system design are introduced. The control laws are demonstrated on a three‐degree‐of‐freedom simulation with linearized aerodynamic and engine models. This paper deals the issue of aircraft landing maneuvers. Generally this part of flight needs to be strongly assisted by human pilot.     

Fuzzy logic based static VAR compensators for enhancing the performance of synchronous and asynchronous motor loads

The paper presents a simple yet powerful fuzzy logic based static VAR compensator (FLSVC) applied to an industrial power network consisting of three phase synchronous and asynchronous motor loads. In the proposed fuzzy logic controller (FLC), the speed and acceleration variations of a specific machine are taken as the inputs. To demonstrate the effectiveness and capabilities of the employed FLSVC, several non-linear time-domain digital simulation tests are performed. The results show that over a wide range of operating conditions and disturbances, the FLSVC improves remarkably the voltage profile and the overall dynamic performance.     

Formation control of VTOL UAV vehicles under switching-directed interaction topologies with disturbance rejection

This paper addresses the adaptive formation control of a group of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV) with switching-directed interaction topologies. In addition, to tackle the adverse effect of disturbances, a couple of smooth bounded estimators are involved in the procedure design. Exploiting an extraction algorithm, we take advantage of the fully actuated rotational dynamics, to control the translational dynamics of each vehicle. We propose a distributed control scheme such that all vehicles track a desired reference velocity signal while keeping a desired prespecified formation. In this framework, the underlying topology of the agents may switch among several directed graphs, each having a spanning tree. The stability of the overall closed-loop system is proved through Lyapunov function. Finally, simulation results are given to better highlight the effectiveness of the proposed control scheme.     

Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations

One of the important operations in nuclear power plants is load-following in which imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation considered to be a constraint for the load-following operation. In this paper, a robust nonlinear model predictive control for the load-following operation problem is proposed that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to maintain xenon oscillations to be bounded. The constant AO is a robust state constraint for load-following problem. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is so effective so that the xenon oscillations kept bounded in the given region.     

An MLP-based representation of neural tensor networks for the RDF data models

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.