System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful

Microscopical evaluation of neural connectivity between paired stages of Eudiplozoon nipponicum (Monogenea : Diplozoidae)

Diplozoidae monogeneans are fish-gill ectoparasites comprising 2 individuals fused in so-called permanent copula. This unique situation occurs when 2 larvae (diporpae) make contact on the host gill, such that their union triggers maturation into an individual adult worm. The present study examined paired stages of Eudiplozoon nipponicum microscopically to ascertain whether somatic fusion involves neural connectivity between these 2 heterogenic larvae. Neuronal pathways were demonstrated in whole-mount preparations of the worm, using indirect immunocytochemical techniques interfaced with confocal scanning laser microscopy for peptidergic and serotoninergic innervations and enzyme cytochemical methodology and light microscopy for cholinergic components. Elements of the central nervous systems of paired worms are connected by commissures the region of fusion so that the 2 systems are in structural continuity. Interindividual connections were most apparent between corresponding ventral nerve cords. All 3 classes of neuronal mediators were identified throughout both central and peripheral connections of the 2 nervous systems. The anatomical complexity and apparent plasticity of the diplozoon nervous system suggest that it has a pivotal role not only in motility, feeding, and reproductive behaviors but also in the events of larval pairing and somatic fusion.

The sites of neural adaptation induced by resistance training in humans

Although it has long been supposed that resistance training causes adaptive changes in the CNS, the sites and nature of these adaptations have not previously been identified. In order to determine whether the neural adaptations to resistance training occur to a greater extent at cortical or subcortical sites in the CNS, we compared the effects of resistance training on the electromyographic (EMG) responses to transcranial magnetic (TMS) and electrical (TES) stimulation. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of 16 individuals before and after 4 weeks of resistance training for the index finger abductors (n=8), or training involving finger abduction-adduction without external resistance (n=8). TMS was delivered at rest at intensities from 5% below the passive threshold to the maximal output of the stimulator. TMS and TES were also delivered at the active threshold intensity while the participants exerted torques ranging from 5 to 60% of their maximum voluntary contraction (MVC) torque. The average latency of MEPs elicited by TES was significantly shorter than that of TMS MEPs (TES latency=21.5+/-1.4 ms; TMS latency=23.4+/-1.4 ms; P

Neural compensation for compliant loads during rhythmic movement

An experiment was performed to characterise the movement kinematics and the electromyogram (EMG) during rhythmic voluntary flexion and extension of the wrist against different compliant (elastic-viscous-inertial) loads. Three levels of each type of load, and an unloaded condition, were employed. The movements were paced at a frequency of I Hz by an auditory metronome, and visual feedback of wrist displacement in relation to a target amplitude of 100degrees was provided. Electro-myographic recordings were obtained from flexor carpi radialis (FCR) and extensor carpi radialis brevis (ECR). The movement profiles generated in the ten experimental conditions were indistinguishable, indicating that the CNS was able to compensate completely for the imposed changes in the task dynamics. When the level of viscous load was elevated, this compensation took the form of an increase in the rate of initial rise of the flexor and the extensor EMG burst. In response to increases in inertial load, the flexor and extensor EMG bursts commenced and terminated earlier in the movement cycle, and tended to be of greater duration. When the movements were performed in opposition to an elastic load, both the onset and offset of EMG activity occurred later than in the unloaded condition. There was also a net reduction in extensor burst duration with increases in elastic load, and an increase in the rate of initial rise of the extensor burst. Less pronounced alterations in the rate of initial rise of the flexor EMG burst were also observed. In all instances, increases in the magnitude of the external load led to elevations in the overall level of muscle activation. These data reveal that the elements of the central command that are modified in response to the imposition of a compliant load are contingent, not only upon the magnitude, but also upon the character of the load.

Neural adaptations to resistance training - Implications for movement control

It has long been believed that resistance training is accompanied by changes within the nervous system that play an important role in the development of strength. Many elements of the nervous system exhibit the potential for adaptation in response to resistance training, including supraspinal centres, descending neural tracts, spinal circuitry and the motor end plate connections between motoneurons and muscle fibres. Yet the specific sites of adaptation along the neuraxis have seldom been identified experimentally, and much of the evidence for neural adaptations following resistance training remains indirect. As a consequence of this current lack of knowledge, there exists uncertainty regarding the manner in which resistance training impacts upon the control and execution of functional movements. We aim to demonstrate that resistance training is likely to cause adaptations to many neural elements that are involved in the control of movement, and is therefore likely to affect movement execution during a wide range of tasks.

Estimation of ternary liquid-liquid equilibria for arene/alkane/ionic liquid mixtures using neural networks

Neural network models have been explored for the prediction of the liquid-liquid equilibrium data and aromatic/aliphatic selectivity values. Four ternary systems composed of toluene, heptane, and the ionic liquids 1-ethyl-3-methylimidazolium ethylsulfate, or 1,3-dimethylimidazolium methylsulfate were investigated at 313.2 and 348.2 K.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.