Development of neural units with higher-order synaptic operations and their applications to logic circuits and control problems

Neural networks play an important role in the execution of goal-oriented paradigms. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. Development of higher-order neural units with higher-order synaptic operations will open a new window for some complex problems such as control of aerospace vehicles, pattern recognition, and image processing. The neural models described in this thesis consider the behavior of a single neuron as the basic computing unit in neural information processing operations. Each computing unit in the network is based on the concept of an idealized neuron in the central nervous system (CNS). Most recent mathematical models and their architectures for neuro-control systems have generated many theoretical and industrial interests. Recent advances in static and dynamic neural networks have created a profound impact in the field of neuro-control. Neural networks consisting of several layers of neurons, with linear synaptic operation, have been extensively used in different applications such as pattern recognition, system identification and control of complex systems such as flexible structures, and intelligent robotic systems. The conventional linear neural models are highly simplified models of the biological neuron. Using this model, many neural morphologies, usually referred to as multilayer feedforward neural networks (MFNNs), have been reported in the literature. The performance of the neurons is greatly affected when a layer of neurons are implemented for system identification, pattern recognition and control problems. Through simulation studies of the XOR logic it was concluded that the neurons with linear synaptic operation are limited to only linearly separable forms of pattern distribution. However, they perform a variety of complex mathematical operations when they are implemented in the form of a network structure. These networks suffer from various limitations such as computational efficiency and learning capabilities and moreover, these models ignore many salient features of the biological neurons such as time delays, cross and self correlations, and feedback paths which are otherwise very important in the neural activity. In this thesis an effort is made to develop new mathematical models of neurons that belong to the class of higher-order neural units (HONUs) with higher-order synaptic operations such as quadratic and cubic synaptic operations. The advantage of using this type of neural unit is associated with performance of the neurons but the performance comes at the cost of exponential increase in parameters that hinders the speed of the training process. In this context, a novel method of representation of weight parameters without sacrificing the neural performance has been introduced. A generalised representation of the higher-order synaptic operation for these neural structures was proposed. It was shown that many existing neural structures can be derived from this generalized representation of the higher-order synaptic operation. In the late 1960’s, McCulloch and Pitts modeled the stimulation-response of the primitive neuron using the threshold logic. Since then, it has become a practice to implement the logic circuits using neural structures. In this research, realization of the logic circuits such as OR, AND, and XOR were implemented using the proposed neural structures. These neural structures were also implemented as neuro-controllers for the control problems such as satellite attitude control and model reference adaptive control. A comparative study of the performance of these neural structures compared to that of the conventional linear controllers has been presented. The simulation results obtained in this research were applicable only for the simplified model presented in the simulation studies

Coalition based approach for shop floor agility – a multiagent approach

Dissertation submitted for a PhD degree in Electrical Engineering, speciality of Robotics and Integrated Manufacturing from the Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThis thesis addresses the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering). One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new processes and equipment. This thesis, therefore, proposes an architecture to support the fast adaptation or changes in the control/supervision architecture. This architecture postulates that manufacturing systems are no more than compositions of modularised manufacturing components whose interactions when aggregated are governed by contractual mechanisms that favour configuration over reprogramming. A multiagent based reference architecture called Coalition Based Approach for Shop floor Agility – CoBASA, was created to support fast adaptation and changes of shop floor control architectures with minimal effort. The coalitions are composed of agentified manufacturing components (modules), whose relationships within the coalitions are governed by contracts that are configured whenever a coalition is established. Creating and changing a coalition do not involve programming effort because it only requires changes to the contract that regulates it

FEATURE SELECTION FOR THE CLASSIFICATION OF LONGITUDINAL HUMAN AGEING DATA

We address the feature selection task in the special context of longitudinal data - where variables are repeatedly measured across different time points. When analysing longitudinal data, a standard feature selection method would typically ignore the temporal nature of the features and treat each feature value at a given time point as a separate feature. That is, a standard algorithm would ignore the important difference between values of the same feature (measuring the same property of an instance) across different time points and values of fundamentally different features (measuring different properties of an instance) at the same time point. This thesis presents two main contributions. The first one is the creation of the longitudinal datasets used in the experiments, including the construction of features capturing longitudinal information for predicting age-related diseases. The datasets were created from data in the English Longitudinal Study of Ageing (ELSA) database. The second contribution consists of proposing four new variants of the Correlation-based Feature Selection (CFS) method for selecting features to be used as input by a classification algorithm. These CFS variants take into account (in different ways) the temporal redundancy associated with variations in the value of a feature across different time points. The results are summarised from two main perspectives. Firstly, in terms of predictive accuracy, one of the proposed CFS variants (called Exh-CFS-Gr - exhaustive search-based CFS per group of temporally redundant features) obtained a statistically significantly better predictive performance than the performance obtained by standard CFS and the baseline approach of no feature selection when using Nai?ve Bayes as the classification algorithm. However, there was no statistically significant difference between the predictive accuracies obtained by J48, a decision tree induction algorithm, for all different variants of CFS (including standard CFS). Secondly, regarding the feature subsets selected by different variants of CFS, the number of features selected by Exh-CFS-Gr was substantially greater than that of all other three CFS variants for all datasets. This helps explaining why this feature selection method obtained the best results in the experiments with Nai?ve Bayes; i.e., it seems that the other CFS variants selected relatively too few features for Nai?ve Bayes. Additionally, the features originally observed in the ELSA database were, in general, selected more often (by all variants of CFS) than the constructed features capturing longitudinal information