Time Series Prediction with a Weighted Bidirectional Multi-Stream Extended Kalman Filter

This paper describes the use of a multi-stream extended Kalman filter (EKF) to tackle the IJCNN 2004 challenge problem - time series prediction on CATS benchmark. A weighted bidirectional approach was adopted in the experiments to incorporate the forward and backward predictions of the time series. EKF is a practical, general approach to neural networks training. It consists of the following: 1) gradient calculation by backpropagation through time (BPTT); 2) weight updates based on the extended Kalman filter; and 3) data presentation using multi-stream mechanics

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results

Value-Gradient Learning

This thesis presents an Adaptive Dynamic Programming method, Value-Gradient Learning, for solving a control optimisation problem, using a neural network to represent a critic function in a large continuous-valued state space. The algorithm developed, called VGL(λ), requires a learned differentiable model of the environment. VGL(λ) is an extension of Dual Heuristic Programming (DHP) to include a bootstrapping parameter, λ, analogous to that used in the reinforcement learning algorithm TD(λ). Online and batch-mode implementations of the algorithm are provided, and its theoretical relationships to its precursor algorithms, DHP and TD(λ), are described. A theoretical result is given which shows that to achieve trajectory optimality in a continuous-valued state space, the critic must learn the value-gradient, and this fact affects any critic-learning algorithm. The connection of this result to Pontryagin's Minimum Principle is made clear. Hence it is proven that learning this value-gradient directly will obviate the need for local exploration of the value function, and this motivates value-gradient learning methods in terms of automatic local value exploration and improved learning speed. Empirical results for the algorithm are given for several benchmark problems, and the improved speed, convergence, and ability to work without local value exploration, is demonstrated in comparison to its precursor algorithms, TD(λ) and DHP. A convergence proof for one instance of the VGL(λ) algorithm is given, which is valid for control problems with a greedy policy, and a general nonlinear function approximator to represent the critic. This is a non-trivial accomplishment, since most or all other related algorithms can be made to diverge under similar conditions, and new divergence proofs demonstrating this for certain algorithms are given in the thesis. Several technical problems must be overcome to make a robust VGL(λ) implementation, and these solutions are described. These include implementing an efficient greedy policy, implementing trajectory clipping correctly, and the efficient computation of second-order gradients with a neural network

Penilaian kepatuhan syariat islam dalam merekabentuk tanah perkuburan islam berkonsepkan taman teknologi

Tanah Perkuburan Islam di Malaysia telah mencapai banyak pembaharuan. Antaranya pembinaan Raudhatul Sakinah iaitu tanah perkuburan dalam taman. Pada peringkat awal, dua tanah perkuburan telah dijadikan tapak pembinaan iaitu Tanah Perkuburan Islam KL-Karak dan Tanah Perkuburan Islam Bukit Kiara yang diuruskan oleh Jabatan Agama Islam Wilayah Persekutuan (JAWI). Lanjutan dari itu sekumpulan penyelidik dari UTHM melakukan penambahbaikan melalui usaha merekabentuk Tanah Perkuburan Islam Berkonsepkan Taman Teknologi menggunakan dengan aplikasi Geographical Information System (GIS) sebagai nilaitambah dalam dalam proses pembinaan tanah perkuburan Islam yang lebih sistematik. Lokasi kajian ini terletak di Tanah Perkuburan Islam, Parit Raja, Batu Pahat. Perkembangan ini memerlukan memerlukan satu garis panduan yang jelas agar usaha yang dilakukan berada dalam ruang lingkup kepatuhan syariat Islam. Justeru kertas kerja ini dihasilkan bagi menilai kepa rekabentuk tanah perkuburan Islam berkonsepkan taman teknologi ini adalah selari dengan ketetapan syariat Islam. Pendekatan kajian ini menggunakan kaedah temubual, permerhatian dan kajian perpustakaan. Hasil dari analisis kajian, terdapat tiga aspek yang perlu diambilkira semasa merekabentuk tanah perkuburan berkonsepkan taman teknologi iaitu tujuan mengkebumikan jenazah, tujuan menziarahi kubur dan bentuk binaan di atas tapak perkuburan. Dapatan daripada kajian ini akan menjadikan rekabentuk Tanah Perkuburan berkonsepkan Taman Teknologi menepati syariat Islam, diterima serta dimanafaatkan oleh seluruh masyarakat Islam di Malaysia

Deep Learning in Target Space

Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to re-parameterise the weights into targets for the firing strengths of the individual nodes in the network. Given a set of targets, it is possible to calculate the weights which make the firing strengths best meet those targets. It is argued that using targets for training addresses the problem of exploding gradients, by a process which we call cascade untangling, and makes the loss-function surface smoother to traverse, and so leads to easier, faster training, and also potentially better generalisation, of the neural network. It also allows for easier learning of deeper and recurrent network structures. The necessary conversion of targets to weights comes at an extra computational expense, which is in many cases manageable. Learning in target space can be combined with existing neural-network optimisers, for extra gain. Experimental results show the speed of using target space, and examples of improved generalisation, for fully-connected networks and convolutional networks, and the ability to recall and process long time sequences and perform natural-language processing with recurrent networks

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise