Kevin Warwick

Artificial intelligence techniques in power systems

* Chapter 1: Artificial intelligence techniques in power systems * Chapter 2: Advanced knowledge engineering techniques with applications to electric power systems * Chapter 3: Object-oriented design and implementation of power system analysis software * Chapter 4: Fuzzy logic and hybrid systems * Chapter 5: Alarm analysis * Chapter 6: Artificial intelligence techniques for voltage control * Chapter 7: AI for protection systems * Chapter 8: Artificial neural networks for static security assessment * Chapter 9: Knowledge based systems for condition monitoring * Chapter 10: Scheduling maintenance of electrical power transmission networks using genetic programming * Chapter 11: Neuro-expert system applications in power systems * Chapter 12: Intelligent systems for demand forcasting * Chapter 13: A practical application and implementation of adaptive techniques using neural networks into autoreclose protection and system control

Neural network applications in control

* Chapter 1: Neural networks: an introduction * Chapter 2: Digital neural networks * Chapter 3: Fundamentals of neurocontrol: a survey * Chapter 4: Selection of neural network structures: some approximation theory guidelines * Chapter 5: Electric power and chemical process applications * Chapter 6: Studies in artificial neural network based control * Chapter 7: Applications of dynamic artificial neural networks in state estimation and nonlinear process control * Chapter 8: Speech, vision and colour applications * Chapter 9: Real-time drive control with neural networks * Chapter 10: Fuzzy-neural control in intensive-care blood pressure management * Chapter 11: Neural networks and system identification * Chapter 12: Neurofuzzy adaptive modelling and construction of nonlinear dynamical processes

Self-tuning regulators—a state space approach

Abstract This paper employs a state space system description to provide a pole placement scheme via state feedback. It is shown that when a recursive least squares estimation scheme is used, the feedback employed can be expressed simply in terms of the estimated system parameters. To complement the state feedback approach, a method employing both state feedback and linear output feedback is discussed. Both methods arc then compared with the previous output polynomial type feedback schemes.

Cyborg morals, cyborg values, cyborg ethics

The era of the Cyborg is now upon us. This has enormous implications on ethical values for both humans and cyborgs. In this paper the state of play is discussed. Routes to cyborgisation are introduced and different types of Cyborg are considered. The authors own self-experimentation projects are described as central to the theme taken. The presentation involves ethical aspects of cyborgisation both as it stands now and those which need to be investigated in the near future as the effects of increased technological power have a more dramatic influence. An important feature is the potential for cyborgs to act against, rather than for, the interests of humanity.

Neural network engineering in dynamic control systems

Neural approximation - a control perspective dynamic systems in neural networks adaptive neurocontrol of a certain class of MIMO discrete-time processes based on stability theory local model architectures for nonlinear modelling and control on ASMOD - an algorithm for empirical modelling using spline functions semi-empirical modelling of nonlinear dynamics systems through identification of operating regimes and local models on interpolating memories of learning control construction and design of parsimonious neurofuzzy systems fast gradient-based off-line training of multilayer perseptrons Kohonen network as a classifier and predictor for the qualification of metal-oxide surfaces analysis and classification of energy requirement situations using Kohonen feature maps within a forecasting system a radial basis function network model for adaptive control of drying oven temperature hierarchical competitive net architecture.

Prediction of Parkinson's disease tremor onset using a radial basis function neural network based on particle swarm optimization.

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinsons disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18-24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinsons disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the networks performance, electromyographic (EMG) signals from the patients forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Emergence of a small-world functional network in cultured neurons.

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks.

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input-output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Parkinson's Disease tremor classification - A comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinsons Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patients brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Industrial Digital Control Systems

The beginnings of radar early history of radar in the US Navy German radar development history of Japanese radar development to 1945 who invented radar? the UK CH ground radar system UK airborne radar RDF and early IFF countermeasure receiver techniques German World War II anti-jamming techniques the post-war years and progress in absolute microwave measurements.