An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This article proposes an IDS model based on a general and enhanced flexible neural tree (FNT). Based on the predefined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, overlayer connections, and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm, and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 337–352, 2007.

The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.

In this article, the fault-tolerant control (FTC) problem of vehicle active suspension is concerned in the discrete-time domain, in which the road disturbances and faults in actuator and measurement are considered. The main contribution consists of proposing an active physically realizable fault-tolerant controller based on a reduced-order observer, which makes up an optimal vibration control component and an event-triggered FTC component. More specifically, by discussing a discrete vehicle active suspension subject to road disturbances generated from the output of a designed exosystem, the optimal vibration control component is derived from maximum principle to offset the inevitable vibrations. Meanwhile, based on the real-time system output of vehicle suspension rather than residual error, a reduced-order observer is proposed to cover the physically unrealizable problem for the designed optimal vibration control component. After that, an event-triggered FTC component and an event-triggered restructured system output are designed to compensate the faults in actuator and measurement, respectively. Finally, extensive experiments are conduced to the control performance of vehicle active suspension under the proposed controller, and confirm its effectiveness and superiority over other control schemes.

Resistive random access memories (RRAMs) have attracted tremendous attention due to their miniaturized size, low power consumption, high response speed, and simple fabrication requirements, which are regarded as a promising candidate for future nonvolatile memories. However, the traditional binary storage RRAMs fail to meet the high-density requirement in the area of big data. One of the ways to overcome this obstacle is to realize multilevel state transitions in a unit RRAM. Herein, we propose a facile spin-coating method to develop a multilevel RRAM based on Al/poly(methyl methacrylate) (PMMA)/ZnO quantum dots (QDs)/PMMA/ZnO QDs/PMMA/fluorine-doped tin oxide (FTO) structure, with the significant merits of typical forming-free bipolar resistive switching behavior, low-power operating voltage, stable endurance, and excellent retention. In specific, the on/off ratio of the proposed ZnO QDs-based device can be adjusted by tuning the concentration of the ZnO QDs. Moreover, a ,,multilevel ,,RRAM performance ,,with distinct four-level resistance states is obtained by varying the compliance current, ,,which demonstrates that the proposed device has a specific application in multilevel data storage. The conductive filament model, combining the enhanced internal field and robust formation of the conductive filament, is introduced to explain the mechanism of the resistive switching behavior and multilevel performance. This work paves a new way for its prospect in constructing high-density data storage RRAMs.