Intelligence for Nonlinear Dynamics and Synchronisation

This chapter sheds light on the application of computational intelligence techniques for various modeling tasks of dynamic systems. In the traditional approach of system engineering, the modeling task is ne-shot experiment. That is, it takes place only once and using some static experimental data that has been ompiled in a previous stage. However, this approach may not be appropriate in situations where the system volves in a dynamically changing environment. The present chapter aims at highlighting the notion of online modeling which is about approximating the system’s behavior in a dynamical and continuous manner. Online odeling is relevant for situations where data arrives over time, the system’s operational mode changes or the environmental conditions change. We mainly focus on dynamic prediction, diagnostic, optimization, and identification.

Synchronous finite state machines are very important for digital sequential designs. Among other important aspects, they represent a powerful way for synchronizing hardware components so that these components may cooperate adequately in the fulfillment of the main objective of the hardware design. In this paper, we propose an evolutionary methodology based on the principles of quantum computing to synthesize finite state machines. First, we optimally solve the state assignment NP-complete problem, which is inherent to designing any synchronous finite state machines. This is motivated by the fact that with an optimal state assignment, one can physically implement the state machine in question using a minimal hardware area and response time. Second, with the optimal state assignment provided, we propose to use the same evolutionary methodology to yield an optimal evolvable hardware that implements the state machine control component. The evolved hardware requires a minimal hardware area and imposes a minimal propagation delay on the machine output signals.

The False Nearest Neighbors (FNN) method is particularly relevant in several fields of science and engineering (medicine, economics, oceanography, biological systems, etc.). In some of these applications, it is important to give results within a reasonable time scale; hence, the execution time of the FNN method has to be reduced. This chapter1 introduces the basic theory and concepts of nonlinear dynamics and chaos, and then describes some parallel implementations of the FNN method for distributed, shared and hybrid memory architectures. The accuracy and performance of the parallel approaches are then assessed and compared with the best sequential implementation of the FNN method, which appears in the TISEAN project.

The DNA microarray technology has shown its extensive applications in clinical research and has emerged as a powerful tool for understanding gene expressions through a simultaneous study of thousands of genes. A successful modeling of gene profiles can provide a pathway of revealing gene regulations from the microarray data. Therefore, modeling the gene expression networks has attracted increasing interests in computational biology community. We propose a nonlinear dynamical system based on kernel auto-regressive model in this application. The proposed method can analyze the nonlinear mapping among the gene expression dynamics by using the kernels. A sparse model is employed so as to decrease the computational cost and improve the illustration ability of the method. We use the kernel recursive least squares, which is an approximation of the kernel principal component analysis, in building the sparse model. By presenting simulation results, we show that dynamical nonlinear networks are attractive and suitable for modeling gene expression profiles. A range of challenging research problems will also be discussed in this paper.

Recent advancements of biometrics identity verification are growing rapidly in this vastly interconnected techno-savvy society. In this information age, protection of valuable contents from the unauthorised intruders or illegal entry to high security zones has made these biometric systems crucial mechanism towards establishing a robust identity verification system. The thrust for reliable authentication methodologies are increasing due to security consciousness of people and also for growing advancement of civilian infrastructures by means of networking, communication, E-Governance, IT knowledge-based civic environment, etc. In the last two decades, a large number of computational intelligence (CI) based and non-linear synchronization based approaches have been thoroughly investigated in biometric authentication in terms of automatic feature detection, feature matching and association of adaptive parameters to the system. Although, it has been felt that the robust and invariant ways are necessary to process the system development from one biometric application to another. However, some incapable and negative constraints have made these biometric systems lack of inconvenience to a large group of end users. To cope up with these incapable factors in biometric systems successfully, Scale Invariant Feature Transform (SIFT) operator has been thoroughly investigated and proved to be invariant to image rotation, scaling, partly illumination changes, biometric authentication towards efficient identity verification.

To achieve high resolution imaging systems, which are desired and often required in many applications, one quickly runs into the problem of diminishing returns. Specifically, the imaging chips and optical components necessary to capture very high-resolution images become prohibitively expensive, costing in the millions of dollars for scientific applications. A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. Due to the consistent development of computer technology in recent years had led to a growing interest in image restoration theory. The main directions are nontraditional treatments to the classic problem and looking at new, second-generation restoration problems, allowing for more complicated and more computationally intensive algorithms. We will demonstrate in this chapter a various methodologies for both spatial and spatial-temporal restoration.

Instability, complexity and chaotic behavior are distinguishing features of financial markets. As a result, financial analysis has greatly benefited from the application of concepts and tools from nonlinear science. In addition, the need to analyze huge amounts of financial data necessitates the utilization of computer-intensive methods. This chapter aims to provide an overview of the diverse research domains, in financial analysis, where nonlinear science, combined with computational intelligence, could find application.

Growing attention in recent decades has been devoted to implementation of methods of computational intelligence for seismic structural control synchronization of buildings and bridges, to reduce their responses to earthquakes. Seismic control synchronization is realized via programmable structural control at seismic excitations, with sensor technologies and synthesis of feedback control loads in regenerative force actuation network for protection of structures. The control synchronization with computational intelligence aims to return a structure with n-degree-of-freedom back to the equilibrium with dynamic switching commutation of actuator devices engaged in regenerative force actuation network. The network consists of a set of electromechanical devices positioned on different places into the structure. The synchronization is realized after activation when these devices absorb and dissipate a part of seismic energy. The actuator devices are connected with each other and their electronic help to share common electrical energy.

Mobile networks in compared to wired networks have unique characteristics. In mobile networks, frequent network topology changes may bring about by node mobility, which are rare in wired networks. In contrast to the stable link capacity of wired networks, wireless link capacity continually varies because of the impacts from transmission power, receiver sensitivity, noise, fading and interference. Moreover, wireless mobile networks have a high error rate, power restrictions and bandwidth limitations.