We propose a new framework for black-box conformance testing of real-time systems, where specifications are modeled as nondeterministic and partially-observable timed automata. We argue that such a model is essential for ease of modeling and expressiveness of specifications. The conformance relation is a timed extension of the inputoutput conformance relation of [29]. We argue that it is better...

Abstract: The Rotary Inverted Pendulum (RIP) system is a significant classical problem of control engineering which has been investigated in the past decades. This study presents an optimum InputOutput Feedback Linearization (IOFL) cascade controller utilized Genetic Algorithm (GA). Due to the non-minimum phase behavior of the system, IOFL controller leads to unstable internal dynamics. Therefo...

Finite-State Transducers (FST) are a standard tool for modeling paired inputoutput sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. [4] presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where t...

The paper describes a novel approach for learning and applying artificial neural network (ANN) models based on incomplete data. A basic novelty in this approach is not to replace the missing part of incomplete data but to train and apply ANN-based models in a way that they should be able to handle such situations. The root of the idea is inherited form the authors’ earlier research for finding ...

This paper presents an adaptive output feedback stabilisation method based on neural networks for nonlinear non-minimum phase systems. The proposed controller comprises a linear, a neuro-adaptive, and an adaptive robustifying parts. The neural network is designed to approximate the matched uncertainties of the system. The inputs of the neural network are the tapped delays of the system inputout...

The NARX network is a dynamical neural architecture commonly used for inputoutput modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward Time Delay Neural Network (TDNN), i.e. without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architectur...

This paper reviews features of a new class of multilayer connectionist architectures known as ASOCS (Adaptive Self-Organizing Concurrent Systems). ASOCS is similar to most decision-making neural network models in that it attempts to learn an adaptive set of arbitrary vector mappings. However, it differs dramatically in its mechanisms. ASOCS is based on networks of adaptive digital elements whic...

System identification is the process of creating models of dynamic process from inputoutput signals. The aim of system identification can be identified as “ to find a model with adjustable parameters and then to adjust them so that the predicted output matches the measured output”. This paper presents a method of modeling and simulating with system identification to achieve the maximum fitness ...

A (combinatorial) channel is a set of pairs of words describing all the possible inputoutput channel situations. We introduce the concept “maximal error-detecting capability” of a given language, with respect to a certain class of channels, which is simply a maximal channel for which the given language is error-detecting. The new concept is intended to address formally the question of “finding ...

In VLSI physical design, a good placement of logic blocks along with the inputoutput blocks around the boundary of the chip can ensure good quality routing of the nets. Even after proper placement of the logic blocks, a random positioning of input-output blocks can result in inefficient and bad quality routing. Here we propose a placement algorithm GENERATE_PAD_FRAME for input-output blocks, fr...