The paper presents briefly a principle functioning of a structure health monitoring (SHM) system which utilizes the phenomenon of elastic waves propagation and soft computing methods. Efficiency and robustness of the design SHM system was verified based on signals measured in laboratory strip specimen. Piezoelectric transducers technology was used here in order to actuate and sense elastic wave...

The primary objective of novelty detection is to examine a system’s dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Mo...

Fuzzy associative memories belong to the class of fuzzy neural networks that employ fuzzy operators such as fuzzy conjunctions, disjunctions, and implications in order to store associations of fuzzy patterns. Fuzzy associative memories are generally used to implement fuzzy rule-based systems. Applications of FAMs include backing up a truck and trailer, target tracking, human-machine interfaces,...

In the framework of autonomous navigation, the use of a cognitive map requires to perform fast and robust storage of different pieces of information. Classical models of autoassociative memory have been proven to be limited in such a context because of the well known catastrophic interference phenomenon. Taking strong inspiration from the inner organization of the hippocampus, we present in thi...

We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of N distinct molecular species. Similar to associative neural networks, the recognition here leverages dynamical attractors to recognize and reconstruct partially corrupted patterns. Traditional parameters of pattern recognition theory, such as sparsity, fidelity, and capacity ...

Recent advances in associative memory design through strutured pattern sets and graph-based inference algorithms have allowed the reliable learning and retrieval of an exponential number of patterns. Both these and classical associative memories, however, have assumed internally noiseless computational nodes. This paper considers the setting when internal computations are also noisy. Even if al...

In this paper, we consider the problem of how to construct an articifial neuronal network such that it reproduces a given set of patterns in an exact manner. Thereby, it turns out that the structure of the weight matrix of the network represents the structure of the set of patterns it is acting on, not the patterns themselves. Moreover, conditions are discussed under which the associative netwo...

this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects th...

This paper considers the robust stability of neural networks with multiple delays. Based on Lyapunov stability theory and linear matrix inequality technique, some new delay independent conditions are derived to guarantee the global robust exponential stability of the equilibrium point. Furthermore, the obtained results are generalized to the interval neural networks and bidirectional associativ...