An iterative learning control theory for a class of nonlinear dynamic systems

An iterative learning control scheme is presented for a class of nonlinear dynamic systems which includes holonomic systems as its subset. The control scheme is composed of two types of control methodology: a linear feedback mechanism and a feedforward learning strategy. At each iteration, the linear feedback provides stability of the system and keeps its state errors within uniform bounds. The iterative learning rule, on the other hand, tracks the entire span of a reference input over a sequence of iterations. The proposed learning control scheme takes into account the dominant system dynamics in its update algorithm in the form of scaled feedback errors. In contrast to many other learning control techniques, the proposed learning algorithm neither uses derivative terms of feedback errors nor assumes external input perturbations as a prerequisite. The convergence proof of the proposed learning scheme is given under minor conditions on the system parameters.     

An extension of the application of information theory to forecasting

This paper presents an optimal forecasting technique from the viewpoint of mutua information. The recommended technique challenges the prevailing method based on the lvu-lma-n filter to the optimality, Test results indicate that the former may expand the latter in the effectiveness of forecasting.     

An overview of statistical learning theory

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems.     

ReliefF-MI: An extension of ReliefF to multiple instance learning

In machine learning the so-called curse of dimensionality, pertinent to many classification algorithms, denotes the drastic increase in computational complexity and classification error with data having a great number of dimensions. In this context, feature selection techniques try to reduce dimensionality finding a new more compact representation of instances selecting the most informative features and removing redundant, irrelevant, and/or noisy features. In this paper, we propose a filter-based feature selection method for working in the multiple-instance learning scenario called ReliefF-MI; it is based on the principles of the well-known ReliefF algorithm. Different extensions are designed and implemented and their performance checked in multiple instance learning. ReliefF-MI is applied as a pre-processing step that is completely independent from the multi-instance classifier learning process and therefore is more efficient and generic than wrapper approaches proposed in this area. Experimental results on five benchmark real-world data sets and 17 classification algorithms confirm the utility and efficiency of this method, both statistically and from the point of view of execution time.     

State-of-health estimator based-on extension theory with a learning mechanism for lead-acid batteries

The main objective of this paper is to design and implement an improved intelligent state-of-health (SOH) estimator for estimating the useful life of lead-acid batteries. Laboratory studies were carried out to measure and record the distributed range of characteristic values in each SOH cycle for the battery subject to cycles of charging and discharging experiments. The measured coup de fouet voltage, internal resistance, and transient current are used as characteristics to develop an intelligent SOH evaluation algorithm. This method is based on the extension matter-element model that has been modified in this research by adding a learning mechanism for evaluation SOH of batteries. The proposed algorithm is relatively simple so that it can be easily implemented with a programmable system-on-chip (PSOC) microcontroller achieve rapid evaluation of battery SOH with precision by using a concise hardware circuit.     

An algebraic theory of object-oriented systems

The paper presents an algebraic specification of net objects. By net objects we mean those that are defined in object-oriented paradigms and those defined with nested relations in complex database models. An algebraic specification is set up that involves structures of net objects, accesses of net objects, and some features of object-oriented programming, such as multiple inheritance and polymorphism. Objects and their relationships are then characterized formally in the modeling, which utilizes the hierarchical approach in the algebraic theory of abstract data types, and is further developed by adding mechanisms from existing object systems. Categories of net objects are presented with the properties of accesses among them     

An extension of A-A-K Hankel approximation theory using state-space formulation

This paper presents a state-space extension of A-A-K Hankel approximation theory. Conventional A-A-K theory can only be applied to solve minimum-degree approximation problems. Here, we extend the A-A-K results and show that it can also be generalized to minimum-norm problems for multivariable systems. State-space formula for generating enough independent Hankel singular vectors corresponding to a prescribed singular value is derived and is used to construct all the optimal Hankel approximants for a given rational function matrix.     

An extension of LaSalle's invariance principle for switched systems

In this paper we address invariance principles for a certain class of switched nonlinear systems. We provide an extension of LaSalle's invariance principle for these systems and state asymptotic stability criteria. We also present some related results on the compactness of the trajectories of these switched systems.     

An introduction to hierarchical systems theory

Hierarchical theory is a new and promising area of general systems theory. This theory deals basically with the decomposition of a system into subsystems forming a hierarchical structure and is, therefore, on method of dealing with complexity. These subsystems or infimals are coordinated by a coordinator or supremal in such a way as to obtain original system objectives. Hence, hierarchical theory is applicable to systems with a natural hierarchical structure or whose dimensionality is so high as to present computational difficulties. Thus it would appear to be particularly appropriate for use in public and societal systems problems.This paper presents a tutorial introduction to hierarchical system theory. Optimization theory is used as a vehicle for presenting the hierarchical concepts, although estimation, identification and other systems problems are also amenable to hierarchical structuring. An example is solved to demonstrate the theory and some computational considerations.     

An extension of predictor control for systems with control time-delays

The control of systems having time-delays in their control paths is considered and feedback laws are developed. A linear system is proposed and a quadratic criterion is optimized over an infinite interval to obtain time-invariant laws. A particularly simple law obtained by the solution of a non-delayed problem can be developed when all controls are delayed by the same amount, but when each control is delayed by a different amount the solution becomes quite complicated. Nevertheless this paper shows how solutions can be obtained by solving a sequence of tracking problems. In contrast to the state-delayed problem, where non-linear partial differential equations need be solved, only ordinary differential equations with single-point boundary values need be solved for the control-delayed problem. The time-invariant feedback law obtained involves a knowledge of the time-delay storages and performs a linear operation on these. The kernels of this operation cannot in general be derived as impulse responses of lumped systems.     

Synchronized extension systems

Synchronized extension systems (SE-systems, for short) are 4-tuples , where V is an alphabet and , and S are languages overV. They generate languages extending by to the left or to the right, and synchronizing on words in S. Such systems appear naturally when considering stacks, queues, grammar-like generative devices, splicing systems, zigzag-codes etc. Received: 1 Feburary 2000 / 16 October 2000     

Service co-creation in social media: An extension of the theory of planned behavior

Social media have become a major channel through which consumers interact with firms and other consumers. This paper examines the factors that drive consumers to co-create in social media and proposes a theoretical model that extends the theory of planned behavior to include perceived usefulness as a key antecedent of consumer attitudes toward co-creation in social media. The model was tested using responses from 743 Chinese consumers. Results of structural equation modeling confirmed that perceived usefulness was an antecedent of consumer attitudes toward co-creation in social media. The relationship between perceived usefulness and customer attitudes toward co-creation was moderated by the level of consumer involvement, and that relationship was found to be more salient for a high rather than a low level of consumer involvement. Practical and theoretical implications are given.     

An embedded system for real-time facial expression recognition based on the extension theory

This paper presents a novel facial expression recognition scheme based on extension theory. The facial region is detected and segmented by using feature invariant approaches. Accurate positions of the lips are then extracted as the features of a face. Next, based on the extension theory, basic facial expressions are classified by evaluating the correlation functions among various lip types and positions of the corners of the mouth. Additionally, the proposed algorithm is implemented using the XScale PXA270 embedded system in order to achieve real-time recognition for various facial expressions. Experimental results demonstrate that the proposed scheme can recognize facial expressions precisely and efficiently.     

An observer theory for time delay systems

A theory of Luenberger type of observers for time-delay systems based on spectral decomposition techniques is developed. The stability, of the observer is rigorously established and the observer equations are reduced to an integro-differential form. The computational aspects are discussed and the difficulty is traced to that of computing eigenvalues of delay differential equations. Formal structural similarities of Kalman filters for delay systems and the observer developed here are indicated.     

Alekseevskii-Tate revisited:An extension to the modified hydrodynamic theory of long rod penetration

The modified hydrodynamic theory of long rod penetration into semi-infinite targets was established independently by Alek-seevskii and Tate over forty years ago and since then many investigators contributed much to the development of the high speed penetration mechanics.However,in all the models proposed so far,the target resistance Rt is not well defined and usually determined by adjusting it until the predicted depth of penetration comes to an agreement with experimental data.In this paper,assumptions are...     

Reactive C: An extension of C to program reactive systems

Reactive systems are interactive programs that react continuously to sequences of activations coming from the external world. Reactive programming leads to a new programming style where one programs in terms of reactions to activations and reasons in a logic of instants. This paper describes an extension of the C programming language called RC (for ‘Reactive C’) to program reactive systems. The language RC is described, then some programming examples are given to illustrate the reactive approach. The main RC notions come directly from the Esterel synchronous programming language. Finally, the Esterel and RC languages are compared.     

User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model

The present study was framed using the Technology Acceptance Model (TAM) to identify determinants affecting behavioral intention to use YouTube. Most importantly, this research emphasizes the motives for using YouTube, which is notable given its extrinsic task goal of being used for procedural learning tasks. Our conceptual framework included two proximal antecedents of behavioral intention as proposed by the TAM – perceived usefulness and perceived ease of use. Additionally, the four first-order constructs of user satisfaction, content richness, vividness, and YouTube self-efficacy, as well as one second-order construct of content richness, were additionally incorporated into the framework by elaborating the theoretical structure. Sample data was collected from 432 respondents who were given the opportunity to engage in procedural learning through YouTube in a lab setting. The results derived from fitting the structural equation model on the sample indicated that behavioral intention was significantly influenced by both perceived usefulness and user satisfaction. Moreover, task-technology fit, content richness, vividness, and YouTube self-efficacy emerged as significant predictors of perceived usefulness. However, perceived ease of use was not significantly predictive of either perceived usefulness or behavioral intention. Our proposed model explained 43.8% of the variance in behavioral intention. Overall findings suggest that YouTube may augment its function as a common channel for procedural learning and instruction.     

A nonlinear extension of the Generalized Hebbian learning

In this letter, we introduce a nonlinear hierarchic PCA type neural network with a simple architecture. The learning algorithm is a kind of nonlinear extension of the well-known Sanger's Generalized Hebbian Algorithm (GHA). It is derived from a nonlinear optimization criterion. Experiments with sinusoidal data show that the neurons become sensitive to different sinusoids. Standard linear PCA algorithms don't have such a separation property.