Detection of incipient (slowly developing) faults is crucial in automated maintenance problems where early detection of worn equipment is required. In this paper, a general framework for model-based fault detection and diagnosis of a class of incipient faults is developed. The changes in the system dynamics due to the fault are modeled as nonlinear functions of the state and input variables, wh...

This paper shows the results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural ne...

LIAO, YI. Neural Networks for Pattern Classification and Universal Approximation (Under the direction of Dr. Shu-Cherng Fang and Dr. Henry L. W. Nuttle). This dissertation studies neural networks for pattern classification and universal approximation. The objective is to develop a new neural network model for pattern classification, and relax the conditions for Radial-Basis Function networks to...

In many reinforcement learning problems, parameters of the model may vary with its phase while the agent attempts to learn through its interaction with the environment. For example, an autonomous car’s reward on selecting a path may depend on traffic conditions at the time of the day or the transition dynamics of a drone may depend on the current wind direction. Many such processes exhibit a cy...

This paper presents a series of new results in modeling of the Grünwald-Letnikov discretetime fractional difference by means of discrete-time Laguerre filers. The introduced Laguerrebased difference LD and combined fractional/Laguerre-based difference CFLD are shown to perfectly approximate its fractional difference original, for fractional order α ∈ 0, 2 . This paper is culminated with the pre...

To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of computational resources (basis functions) many researchers are investigating ways to adapt the basis functions during the learning process so that they better t the value-function landscape. Here we introduce temporal neig...

In complex real-world environments, traditional (tabular) techniques for solving Reinforcement Learning (RL) do not scale. Function approximation is needed, but unfortunately, existing approaches generally have poor convergence and optimality guarantees. Additionally, for the case of human environments, it is valuable to be able to leverage human input. In this paper we introduce Expanding Valu...

It is well known that universal approximators can be used for adaptive control and estimation. In this paper, the problem of adaptive state observation of a large class of nonlinear uncertain systems is considered and it is shown that splines have some special properties, which can lead to simplified observer structure. In particular, the observer filter has fixed dynamical order, independent o...

In knowledge representation, when we have to use logical connectives, various continuous t-norms and t-conorms are used. In this paper, we show that every continuous t-norm and t-conorm can be approximated, to an arbitrary degree of accuracy, by a strict Archimedean t-norm (t-conorm).

For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural man-made materials in response mechanical loading. Now, computer science offers a universal solution: Neural Networks. Networks are powerful function approximators that can learn relations from large data without any knowledge underlying p...