In this paper, an innovative robust adaptive tracking control method for robotic systems with unknown dynamics using a nonlinearly parameterized Additive Recurrent Neural Network (ARNN) is proposed. The ARNN uses the Gaussian Radial Basis Functions (GRBF) as activation functions. Through this method the training laws of all GRBF parameters are determined. Additionally, the system is augmented w...

The value algorithms of classical function approximation theory have a common drawback: the compute-intensive, poor adaptability, high model and data demanding and the limitation in practical applications. Neural network can calculate the complex relationship between input and output, therefore, neural network has a strong function approximation capability. This paper describes the application ...

The SVM has recently been introduced as a new learning technique for solving variety of real world applications based on learning theory. The classical RBF network has similar structure as SVM with Gaussian kernel. Similarly, the FNN also possess an identical structure with SVM. The support vector machine includes polynomial learning machine, radial-basis function network, Gaussian radial-basis...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process. We first consider the case of jump-diffusion processes, where the drift of a linear stochastic differential equation can jump at arbitrary time points. We derive partial differential equations for exact inference and present a very effici...

The security of computer networks plays a strategic role in modern computer systems. Intrusion Detection Systems (IDS) act as the ‘‘second line of defense’’ placed inside a protected network, looking for known or potential threats in network traffic and/or audit data recorded by hosts. We developed an Intrusion Detection System using LAMSTAR neural network to learn patterns of normal and intrus...

We start by noting that conditional independence X ⊥ Y |B⊤X does not necessarily imply the correlation between BX and Y is maximized. To see this, let X be a Gaussian random vairable with zero mean and diagonal covariance matrix. AssumeB is an identity matrix and Y = X = (BX) (elementwise square for a vectorial X). The conditional independence is obviously satisfied yet the correlation between ...

This paper is concerned with the comparison of three types of Gaussian based Artificial Neural Networks in the very high dimensionality classification problems found in hyperspectral signal processing. In particular, they have been compared for the spectral unmixing problem given the fact that the requirements for this type of classification are very different from other realms in two aspects: ...

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows us to gain the generalisation benefit of a convolutional kernel, together wit...

We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is h...