Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

Intelligent joint channel parameter estimation techniques for mobile wireless positioning applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile wireless positioning has recently received great attention. For mobile wireless communication networks, an inherently suitable approach is to obtain the parameters that are used for positioning estimates from the radio signal measurements between a mobile device and one or more xed base stations. However, obtaining accurate estimates of these location-dependent channel parameters is a challenging task. The focus of this thesis is on the estimation of these channel parameters for mobile wireless positioning applications. In particular, we investigate novel estimators that jointly estimate more than one type of channel parameters. We rst perform a comprehensive critical review on the most recent and popular joint channel parameter estimation techniques. Secondly, we improve a state-of-the-art technique, namely the Space Alternating Generalised Expectation maximisation (SAGE) algorithm by employing adaptive interference cancellation to improve the estimation accuracy of weaker paths. Thirdly, a novel intelligent channel parameter estimation technique using Evolution Strategy (ES) is proposed to overcome the drawbacks of the existing iterative maximum likelihood methods. Furthermore, given that in reality it is di cult to obtain the number of multipath in advance, we propose a two tier Hierarchically Organised ES to jointly estimate the number of multipath as well as the channel parameters. Finally, we extend the proposed ES method to further estimate the Doppler shift in mobile environments. Our proposed intelligent joint channel estimation techniques are shown to exhibit excellent performance even with low Signal to Noise Ratio (SNR) channel conditions as well as robust against uncertainties in initialisations.EPSRC and Cambridge Silicon Radi

Modeling and identification of a gene regulatory network programming erythropoiesis (1)

The development of mature blood cells of distinct lineages from the hematopoietic stem cells (hematopoiesis) involves a progressive restriction of differentiation potential and the establishment of lineage-specific gene expression profiles. The establishment of these profiles relies on lineage-specific transcription factors to modulate the expression of their target genes. This work is embedded in a wider ErasmusMC/CWI collaboration that develops the informatics and mathematics to underpin studies on gene expression regulation by mapping and analyzing the regulatory pathways and networks of transcription factors that control cellular functions (so called 'Gene Regulatory Networks' or 'GRNs'). This project is concerned with the mathematical part and concentrates on a GRN central to erythropoiesis. Among the many housekeeping and tissue-specific genes involved in the differentiation and the commitment of hematopoietic stem cells to erythrocytes (erythropoiesis), we focus on a small pool of genes (Gata-1, Gata-2, Pu.1, EKLF, FOG-1, alpha/beta-globin) known to be critically involved in an intricate but well-less investigated regulatory circuit. Based on the regulatory interactions in the GRN we have developed models in the form of a system to account for the dynamics of gene expression and regulation involved in this process. Because of the lack of information about a significant number of model parameters, our focus is on system identification. In this first report some preliminary results are presented based on synthetic data. However, time series of the levels of all relevant mRNA’s are available from micro-array analysis of G1E cells, a murine cell line which recapitulates erythropoiesis. In the follow-up report a detailed account will be given of the parameter estimation and identifiability analysis with respect to these data. This will eventually allow for a thorough evaluation of the role of various characterized as well as hypothetical regulatory mechanisms. In depth characterization of the necessary expression patterns and gene regulatory interactions responsible for the the set of commitments all along the erythroid lineage is essential to gain fundamental insight into the behaviour of these complex networks and to design further experiments. Ultimately, this may lead to ways to rescue erythroid differentiation in several anemic diseases

Investigating hybrids of evolution and learning for real-parameter optimization

In recent years, more and more advanced techniques have been developed in the field of hybridizing of evolution and learning, this means that more applications with these techniques can benefit from this progress. One example of these advanced techniques is the Learnable Evolution Model (LEM), which adopts learning as a guide for the general evolutionary search. Despite this trend and the progress in LEM, there are still many ideas and attempts which deserve further investigations and tests. For this purpose, this thesis has developed a number of new algorithms attempting to combine more learning algorithms with evolution in different ways. With these developments, we expect to understand the effects and relations between evolution and learning, and also achieve better performances in solving complex problems. The machine learning algorithms combined into the standard Genetic Algorithm (GA) are the supervised learning method k-nearest-neighbors (KNN), the Entropy-Based Discretization (ED) method, and the decision tree learning algorithm ID3. We test these algorithms on various real-parameter function optimization problems, especially the functions in the special session on CEC 2005 real-parameter function optimization. Additionally, a medical cancer chemotherapy treatment problem is solved in this thesis by some of our hybrid algorithms. The performances of these algorithms are compared with standard genetic algorithms and other well-known contemporary evolution and learning hybrid algorithms. Some of them are the CovarianceMatrix Adaptation Evolution Strategies (CMAES), and variants of the Estimation of Distribution Algorithms (EDA). Some important results have been derived from our experiments on these developed algorithms. Among them, we found that even some very simple learning methods hybridized properly with evolution procedure can provide significant performance improvement; and when more complex learning algorithms are incorporated with evolution, the resulting algorithms are very promising and compete very well against the state of the art hybrid algorithms both in well-defined real-parameter function optimization problems and a practical evaluation-expensive problem

Control techniques for mechatronic assisted surgery

The treatment response for traumatic head injured patients can be improved by using an autonomous robotic system to perform basic, time-critical emergency neurosurgery, reducing costs and saving lives. In this thesis, a concept for a neurosurgical robotic system is proposed to perform three specific emergency neurosurgical procedures; they are the placement of an intracranial pressure monitor, external ventricular drainage, and the evacuation of chronic subdural haematoma. The control methods for this system are investigated following a curiosity led approach. Individual problems are interpreted in the widest sense and solutions posed that are general in nature. Three main contributions result from this approach: 1) a clinical evidence based review of surgical robotics and a methodology to assist in their evaluation, 2) a new controller for soft-grasping of objects, and 3) new propositions and theorems for chatter suppression sliding mode controllers. These contributions directly assist in the design of the control system of the neurosurgical robot and, more broadly, impact other areas outside the narrow con nes of the target application. A methodology for applied research in surgical robotics is proposed. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers. However, the lack of conformity to the criteria in the top tier, combined with the inability to conclusively prove increased clinical benefit, particularly symptomatic benefit, is shown to be hampering the potential of surgical robotics in gaining wide establishment. A control scheme for soft-grasping objects is presented. Grasping a soft or fragile object requires the use of minimum contact force to prevent damage or deformation. Without precise knowledge of object parameters, real-time feedback control must be used to regulate the contact force and prevent slip. Moreover, the controller must be designed to have good performance characteristics to rapidly modulate the fingertip contact force in response to a slip event. A fuzzy sliding mode controller combined with a disturbance observer is proposed for contact force control and slip prevention. The robustness of the controller is evaluated through both simulation and experiment. The control scheme was found to be effective and robust to parameter uncertainty. When tested on a real system, however, chattering phenomena, well known to sliding mode research, was induced by the unmodelled suboptimal components of the system (filtering, backlash, and time delays). This reduced the controller performance. The problem of chattering and potential solutions are explored. Real systems using sliding mode controllers, such as the control scheme for soft-grasping, have a tendency to chatter at high frequencies. This is caused by the sliding mode controller interacting with un-modelled parasitic dynamics at the actuator-input and sensor-output of the plant. As a result, new chatter-suppression sliding mode controllers have been developed, which introduce new parameters into the system. However, the effect any particular choice of parameters has on system performance is unclear, and this can make tuning the parameters to meet a set of performance criteria di cult. In this thesis, common chatter-suppression sliding mode control strategies are surveyed and simple design and estimation methods are proposed. The estimation methods predict convergence, chattering amplitude, settling time, and maximum output bounds (overshoot) using harmonic linearizations and invariant ellipsoid sets