In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learnin...

Abstract Neural networks (NN) are taking over ever more decisions thus far taken by humans, even though verifiable system-level guarantees out of reach. Neither is the verification technology available, nor it understood what a formal, meaningful, extensible, and scalable testbed might look like for such technology. The present paper an attempt to improve on both above aspects. We family formal...

The application of neural networks (NNs) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chr...

The application of neural networks (NN’s) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art ch...

Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are neg...

Growth performance, carcass characteristics, and meat quality of halothane carrier (Nn) and negative (NN) pigs grown from 40 kg live weight and slaughtered at three weights (110, 125, and 140 kg live weight) were studied. Daily gains were similar for the two genotypes (974 g for Nn and 964 g for NN), but Nn pigs had a higher gain:feed ratio than NN pigs (P < .01). Dressing percentage was higher...

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can’t be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly...

In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...

The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a wide scope of discrete and continuous design variables that can be adjusted to increase performan...

k-nearest neighbor (k-NN) classi"cation is a well-known decision rule that is widely used in pattern classi"cation. However, the traditional implementation of this method is computationally expensive. In this paper we develop two e5ective techniques, namely, template condensing and preprocessing, to signi"cantly speed up k-NN classi"cation while maintaining the level of accuracy. Our template c...