Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic an...

Driving a realistic facial animation with Support Vector Machine(SVM) requires determining the shape-to-wrinkle correspondence, which includes not only spatial dependency, but also temporal dependency. A few available frameworks(e.g., Recurrent Neural Network and Long Short-Term Memory), represent temporal dependency as the dependency of output on position input series, which however may bring ...

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of t...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation leamng method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of two...

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A interval type-2 recurrent fuzzy neural system with asymmetric membership function , for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information ...

The Call admission control (CAC) is one of the Radio Resource Management (RRM) techniques that plays influential role in ensuring the desired Quality of Service (QoS) to the users and applications in next generation networks. This paper proposes a fuzzy neural approach for making the call admission control decision in multi class traffic based Next Generation Wireless Networks (NGWN). The propo...

In this paper, we propose a hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization algorithm (PSO) algorithms to design the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS) for application of nonlinear system control. The FLPRFNS has a TSK-type fuzzy consequent part which uses functional-link based orthogonal basis functions and a Petri...

The identi"cation and simulation of dynamic systems is still a challenging problem. In this article some basic aspects of neuro-fuzzy techniques for the identi"cation and simulation of time-dependent physical systems are presented. In particular, a neuro-fuzzy model that can be used for the identi"cation and the (real-time) simulation of viscoelastic models, is described. The presented model is...