Quality of neural network mappings may be evaluated by visual inspection of hidden and output node activities for the training dataset. This paper discusses how to visualize such multidimensional data, introducing a new projection on a lattice of hypercube nodes. It also discusses what type of information one may expect from visualization of the activity of hidden and output layers. Detailed an...

Critical care providers are faced with resource shortages and must find ways to effectively plan their resource utilization. Neural networks provide a new method for evaluating trauma patient (and other medical patient) level of illness and accurately predicting a patient’s length of stay at the critical care facility. Backpropagation, radial-basis-function, and fuzzy ARTMAP neural networks are...

In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using th...

Pattern classification was an important part of the RBF neural network application. When the electronic nose is concerned, in many cases it is difficult to obtain the entire representative sample; it requires frequent updating the sample libraries and re-training the electronic nose. In addition,the gas detected from the online environment is not always the known gas in the training samples. Th...

In this paper, we present a computing model for diagnosis and prescription in oriental medicine. Inputs to the model are severities of symptoms observed on patients and outputs from the model are a diagnosis of disease states and treatment herbal prescriptions. First, having used rule inference with a Gaussian distribution, the most serious disease state in which the patient appears to be infec...

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial bas...

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural netw...

Radial neural network can be used to decompose complex problems with good biological properties. Adaptive control for neural network is helpful to improve the efficiency of pattern classification. In order to solve pattern classification with different adaptive characteristics, multiple adaptive algorithms are embedded in radial neural network. Through the test of the objective function, it is ...

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...