Identification of Induction Machines Stator Currents with Generalized Neurons

A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system

Real-Time Implementation of a Dual Function Neuron Based Wide Area SVC Damping Controller

The use of wide area measurements for power system stabilization is recently given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts such as the August 2003. This paper presents the design of a nonlinear external damping controller based on wide area measurements as inputs to a dual function neuron (DFN). This DFN controller is specifically designed to enhance the damping characteristics of a power system considering the nonlinearity in the system. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller is implemented on a digital signal processor and its performance is evaluated on the IEEE 12 bus FACTS benchmark power system implemented on a real time platform - real time digital simulator (RTDS). Experimental results show that the DFN controller provides better damping than a conventional linear controlle

Fuzzy Systems

This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

<p>Abstract</p> <p>Background</p> <p>Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.</p> <p>Methods</p> <p>In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat.</p> <p>Results</p> <p>The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively.</p> <p>Conclusion</p> <p>The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.</p

INTERVAL TYPE-2 FUZZY MODEL FOR CUSTOMER COMPLAINT HANDLING

Complaint management system (CMS) has become increasingly important for organizations, businesses, and government in Malaysia. The interaction between customers and business provider based on complaints which referring to perceptions and wording involves uncertainties and not an easy task in complaint handling process to rank the complaint

Multiple instance fuzzy inference.

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Fuzzy Inference Systems (MI-FIS). Fuzzy inference is a powerful modeling framework that can handle computing with knowledge uncertainty and measurement imprecision effectively. Fuzzy Inference performs a non-linear mapping from an input space to an output space by deriving conclusions from a set of fuzzy if-then rules and known facts. Rules can be identified from expert knowledge, or learned from data. In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. In this dissertation, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, different multiple instance fuzzy inference styles are proposed. The Multiple Instance Mamdani style fuzzy inference (MI-Mamdani) extends the standard Mamdani style inference to compute with multiple instances. The Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) is an extension of the standard Sugeno style inference to handle reasoning with multiple instances. In addition to the MI-FIS inference styles, one of the main contributions of this work is an adaptive neuro-fuzzy architecture designed to handle bags of instances as input and capable of learning from ambiguously labeled data. The proposed architecture, called Multiple Instance-ANFIS (MI-ANFIS), extends the standard Adaptive Neuro Fuzzy Inference System (ANFIS). We also propose different methods to identify and learn fuzzy if-then rules in the context of MIL. In particular, a novel learning algorithm for MI-ANFIS is derived. The learning is achieved by using the backpropagation algorithm to identify the premise parameters and consequent parameters of the network. The proposed framework is tested and validated using synthetic and benchmark datasets suitable for MIL problems. Additionally, we apply the proposed Multiple Instance Inference to the problem of region-based image categorization as well as to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols