In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) in multiple classification tasks. In this paper, we propose and apply AAFs on feedforward NNs for regression tasks. We argue that applying AAFs in t...

This paper presents an iterative learning controller using neural network (NN) for the robot trajectory tracking control. Basic control configuration is briefly presented and a new weight-tuning algorithm of NN is proposed with a dead-zone technique. Theoretical proof is given which shows that our modified algorithm guarantees the convergence of NN estimation error in the presence of disturbanc...

A neural network (NN) Modeling is presented based on biological and electrical behaviors. The proposed model is given with forward and reverse couplings with positive and negative signs. This model corresponds to the actual synaptic coupling of neurons. Neurons are connected mutually and allocated multiple accesses, total large numbers of neurons are included in the NN is the second advantage. ...

We have found that the nearest neighbor (NN) test is an insufficient measure of the cluster hypothesis. The NN test is a local measure of the cluster hypothesis. Designers of new document-to-document similarity measures may incorrectly report effective clustering of relevant documents if they use the NN test alone. Utilizing a measure from network analysis, we present a new, global measure of t...

This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models ...

The aim of our research is to classify digital mammograms into two classes, abnormal microcalcification and normal. Texture is one of the major mammographic characteristics. The statistical textural of Gray Level Coocurrence Matrix (GLCM) used in characterizing images are contrast, energy and entropy. K-Nearest Neighbor (K-NN) and Fuzzy K-Nearest Neighbor (FK-NN) was proposed for classifying im...

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection S...

The architecture named the GALIS is a cluster-based distributed computing system architecture which has been devised to efficiently handle a large volume of LBS application data. In this paper, we propose a distributed kNN query processing scheme for moving objects on multiple computing nodes, each of which keeps records relevant to a different geographical zone. We also propose a hybrid k-NN s...

A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator ...

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. The considere...