International Journal of Computational Intelligence and Applications

This paper presents an adaptive moving sliding mode fuzzy control for robotic manipulators. The consequence parameters of the fuzzy control rules are adjusted online by applying the Lyapunov stability condition. Furthermore, parameters tuning is carried out in order to guarantee the sliding condition. Comparison between zero order and first order Sugeno model are presented. Moreover, a time-varying sliding surface is used with the aim of enhancing tracking-performance. Indeed, moving sliding surface improves the robustness during the reaching phase against uncertainties, parameter variations and extraneous disturbances. As an illustration, the trajectory control of a two degrees-of-freedom robotic manipulator is considered.

We describe a generalisation of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results on data from robot vision, content-based image retrieval, and protein sequence identification.

This paper introduces a new ensemble technique, cluster-based concurrent decomposition (CBCD) that induces an ensemble of classifiers by decomposing the training set into mutually exclusive sub-samples of equal-size. The CBCD algorithm first clusters the instance space by using the K-means clustering algorithm. Afterwards it produces disjoint sub-samples using the clusters in such a way that each sub-sample is comprised of tuples from all clusters and hence represents the entire dataset. An induction algorithm is applied in turn to each subset, followed by a voting mechanism that combines the classifier's predictions. The CBCD algorithm has two tuning parameters: the number of clusters and the number of subsets to create. Using a suitable meta-learning it is possible to tune these parameters properly. In the experimental study we conducted, the CBCD algorithm, using an embedded C4.5 algorithm, outperformed the bagging algorithm of the same computational complexity.

In this paper, a system that can automatically detect and recognise frontal faces is proposed. Three methods are used for face recognition; neural network, template matching and distance measure. One of the main problems encountered when using neural networks for face recognition is insufficient training data. This problem arises because, in most cases, only one image per subject is available. Therefore, amongst the objectives is to solve this problem by "increasing" the data available from the original image using several preprocesses, for example, image mirroring, colour and edges information, etc. Moreover, template matching is not trivial because of differences in the template shapes and sizes. In this work, template matching is aided by a genetic algorithm to automatically test several positions around the target and automatically adjust the size of the template as the matching process progresses. Distance measure method depends heavily on good facial feature extraction results. The image segmentation method applied matches such demand. The face colour information is represented using YIQ and the XYZ colour spaces. The effectiveness of the proposed method is verified by performing computer simulations. Two sets of databases were used. Database1 consists of 267 faces from the Oulu university database and database2 (for comparision purposes) consists of 250 faces from the ORL database.

IP address lookup engine is the beating heart of a router. For meeting the requirements of a desirable high-speed router, speed, memory consumption, scalability, and reconfigurability of its IP lookup engine are critical. This paper uses a genetic-algorithm approach to optimise the structure of fixed-stride multibit-trie IP lookup methods. In this work, the genetic algorithm is used first in the design phase as an offline optimisation mechanism. This nature-inspired simulation finds the most memory-efficient configuration of IP address segmentation for a fixed number of address segments. Then, for adapting to network variations, the proposed method dynamically changes the number of address segments to compromise between speed and memory consumption. Each time the number of segments changes, an online genetic program is run to optimise the segmentation. Therefore, the lookup engine reconfigures itself to cover more prefixes during the time. The reconfigurability in response to the network variations, and scalability to the number of prefixes improves the life time of a router that uses this method.

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.