Learning in Single Hidden Layer Feedforward Network Models: Backpropagation in a Real World Application

Leaming in neural networks has attracted considerable interest in recent years. Our focus is on learning in single hidden layer feedforward networks which is posed as a search in the network parameter space for a network that minimizes an additive error function of statistically independent examples. In this contribution, we review first the class of single hidden layer feedforward networks and characterize the learning process in such networks from a statistical point of view. Then we describe the backpropagation procedure, the leading case of gradient descent learning algorithms for the class of networks considered here, as well as an efficient heuristic modification. Finally, we analyse the applicability of these learning methods to the problem of predicting interregional telecommunication flows. Particular emphasis is laid on the engineering judgment, first, in choosing appropriate values for the tunable parameters, second, on the decision whether to train the network by epoch or by pattern (random approximation), and, third, on the overfitting problem. In addition, the analysis shows that the neural network model whether using either epoch-based or pattern-based stochastic approximation outperforms the classical regression approach to modelling telecommunication flows. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Neural Network Modelling of Constrained Spatial Interaction Flows

Fundamental to regional science is the subject of spatial interaction. GeoComputation - a new research paradigm that represents the convergence of the disciplines of computer science, geographic information science, mathematics and statistics - has brought many scholars back to spatial interaction modeling. Neural spatial interaction modeling represents a clear break with traditional methods used for explicating spatial interaction. Neural spatial interaction models are termed neural in the sense that they are based on neurocomputing. They are clearly related to conventional unconstrained spatial interaction models of the gravity type, and under commonly met conditions they can be understood as a special class of general feedforward neural network models with a single hidden layer and sigmoidal transfer functions (Fischer 1998). These models have been used to model journey-to-work flows and telecommunications traffic (Fischer and Gopal 1994, Openshaw 1993). They appear to provide superior levels of performance when compared with unconstrained conventional models. In many practical situations, however, we have - in addition to the spatial interaction data itself - some information about various accounting constraints on the predicted flows. In principle, there are two ways to incorporate accounting constraints in neural spatial interaction modeling. The required constraint properties can be built into the post-processing stage, or they can be built directly into the model structure. While the first way is relatively straightforward, it suffers from the disadvantage of being inefficient. It will also result in a model which does not inherently respect the constraints. Thus we follow the second way. In this paper we present a novel class of neural spatial interaction models that incorporate origin-specific constraints into the model structure using product units rather than summation units at the hidden layer and softmax output units at the output layer. Product unit neural networks are powerful because of their ability to handle higher order combinations of inputs. But parameter estimation by standard techniques such as the gradient descent technique may be difficult. The performance of this novel class of spatial interaction models will be demonstrated by using the Austrian interregional traffic data and the conventional singly constrained spatial interaction model of the gravity type as benchmark. References Fischer M M (1998) Computational neural networks: A new paradigm for spatial analysis Environment and Planning A 30 (10): 1873-1891 Fischer M M, Gopal S (1994) Artificial neural networks: A new approach to modelling interregional telecommunciation flows, Journal of Regional Science 34(4): 503-527 Openshaw S (1993) Modelling spatial interaction using a neural net. In Fischer MM, Nijkamp P (eds) Geographical information systems, spatial modelling, and policy evaluation, pp. 147-164. Springer, Berlin

Augmented reality system for rehabilitation : new approach based on human interaction and biofeedback

University of Technology Sydney. Faculty of Engineering and Information Technology.Rehabilitation is the process of training for someone in order to recover or improve their lost functions caused by neurological deficits. The upper limb rehabilitation system provides relearning of motor skills that are lost due to any neurological injuries via motor rehabilitation training. The process of motor rehabilitation is a form of motor learning via practice or experience. It requires thorough understanding and examination of neural processes involved in producing movement and learning as well as the medical aspects that may affect the central nervous system (CNS) or peripheral nervous system (PNS) in order to develop an effective treatment system. Although there are numerous rehabilitation systems which have been proposed in literatures, a low cost upper limb rehabilitation system that maximizes the functional recovery by stimulating the neural plasticity is not widely available. This is due to lack of motivation during rehabilitation training, lack of real time biofeedback information with complete database, the requirement of one to one attention between physiotherapist and patient, the technique to stimulate human neural plasticity. Therefore, the main objective of this thesis is to develop a novel low cost rehabilitation system that helps recovery not only from loss of physical functions, but also from loss of cognitive functions to fulfill the aforementioned gaps via multimodal technologies such as augmented reality (AR), computer vision and signal processing. In order to fulfill such ambitious objectives, the following contributions have been implemented. Firstly, since improvements in physical functions are targeted, the Rehabilitation system with Biofeedback simulation (RehaBio) is developed. The system enhances user’s motivation via game based therapeutic exercises and biofeedback. For this, AR based therapeutic games are developed to provide eye-hand coordination with inspiration in motivation via immediate audio and visual feedback. All the exercises in RehaBio are developed in a safe training environment for paralyzed patients. In addition to that, real-time biofeedback simulation is developed and integrated to serve in two ways: (1) from the patient’s point of view, the biofeedback simulation motivates the user to execute the movements since it will animate the different muscles in different colors, and (2) from the therapist’s point of view, the muscle simulations and EMG threshold level can be evaluated as patient’s muscle performance throughout the rehabilitation process. Secondly, a new technique that stimulates the human neural plasticity is proposed. This is a virtual human arm (VHA) model that driven by proposed continuous joint angle prediction in real time based on human biological signal, Electromyogram (EMG). The VHA model simulation aims to create the illusion environment in Augmented Reality-based Illusion System (ARIS). Finally, a complete novel upper limb rehabilitation system, Augmented Reality-based Illusion System (ARIS) is developed. The system incorporates some of the developments in RehaBio and real time VHA model to develop the illusion environment. By conducting the rehabilitation training with ARIS, user’s neural plasticity will be stimulated to re-establish the neural pathways and synapses that are able to control mobility. This is achieved via an illusion concept where an illusion scene is created in AR environment to remove the impaired real arm virtually and replace it with VHA model to be perceived as part of the user’s own body. The job of the VHA model in ARIS is when the real arm cannot perform the required task, it will take over the job of the real one and will let the user perceive the sense that the user is still able to perform the reaching movement by their own effort to the destination point. Integration with AR based therapeutic exercises and motivated immediate intrinsic and extrinsic feedback in ARIS leads to serve as a novel upper limb rehabilitation system in a clinical setting. The usability tests and verification process of the proposed systems are conducted and provided with very encouraging results. Furthermore, the developments have been demonstrated to the clinical experts in the rehabilitation field at Port Kembla Hospital. The feedback from the professionals is very positive for both the RehaBio and ARIS systems and they have been recommended to be used in the clinical setting for paralyzed patients

Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks

Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. These approaches are referred to as support vector sequential feed-forward neural networks (SV-SFNNs), and they are a particular case of the sequential approximation with optimal coefficients and interacting frequencies (SAOCIF) method. In this paper, it is firstly shown that EM-ELMs can also be cast as a particular case of SAOCIF. In particular, EM-ELMs can easily be extended to test some number of random candidates at each step and select the best of them, as SAOCIF does. Moreover, it is demonstrated that the cost of the computation of the optimal output-layer weights in the originally proposed EM-ELMs can be improved if it is replaced by the one included in SAOCIF. Secondly, we present the results of an experimental study on 10 benchmark classification and 10 benchmark regression data sets, comparing EM-ELMs and SV-SFNNs, that was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in 12 out of the 20 benchmark problems. © 2011 Elsevier Ltd.This work was supported in part by the Ministerio de Ciencia e Innovación (MICINN), under project TIN2009-13895-C02-01.Peer Reviewe