In this article, a hierarchical classifier is proposed for classification of ground-cover types of a satellite image of Kangaroo Island, South Australia. The image contains seven ground-cover types, which are categorized into three groups using principal component analysis. The first group contains clouds only, the second consists of sea and cloud shadow over land, and the third contains land and three types of forest. The sea and shadow over land classes are classified with 99% accuracy using a network of threshold logic units. The land and forest classes are classified by multilayer perceptrons (MLPs) using texture features and intensity values. The average performance achieved by six trained MLPs is 91%. In order to improve the classification accuracy even further, the outputs of the six MLPs were combined using several committee machines. All committee machines achieved significant improvement in performance over the multilayer perceptron classifiers, with the best machine achieving over 92% correct classification.

A multilayer perceptron is usually considered a passive learner that only receives given training data. However, if a multilayer perceptron actively gathers training data that resolve its uncertainty about a problem being learnt, sufficiently accurate classification is attained with fewer training data. Recently, such active learning has been receiving an increasing interest. In this paper, we propose a novel active learning strategy. The strategy attempts to produce only useful training data for multilayer perceptrons to achieve accurate classification, and avoids generating redundant training data. Furthermore, the strategy attempts to avoid generating temporarily useful training data that will become redundant in the future. As a result, the strategy can allow multilayer perceptrons to achieve accurate classification with fewer training data. To demonstrate the performance of the strategy in comparison with other active learning strategies, we also propose an empirical active learning algorithm as an implementation of the strategy, which does not require expensive computations. Experimental results show that the proposed algorithm improves the classification accuracy of a multilayer perceptron with fewer training data than that for a conventional random selection algorithm that constructs a training data set without explicit strategies. Moreover, the algorithm outperforms typical active learning algorithms in the experiments. Those results show that the algorithm can construct an appropriate training data set at lower computational cost, because training data generation is usually costly. Accordingly, the algorithm proves the effectiveness of the strategy through the experiments. We also discuss some drawbacks of the algorithm.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.