Search

Bus rapid transit (BRT) has been proved to be an effective tool to improve mass transit services. However, BRT's adaptive operations like management and scheduling under different scenarios are too complicated to implement using traditional methods. The ACP approach, which is based on holism and complex system theory and consists of artificial systems (A), computational experiments (C) and parallel execution (P), offers an efficient new method to cope with these complex systems, including BRT. In this paper, the parallel transportation management and control system for BRT (PTMS-BRT) is presented, which is designed and implemented using the ACP approach. PTMS-BRT integrates such functions as BRT's monitoring, warning, forecasting, incident management, and real-time scheduling, to provide its operations smoother, safer, more efficient, and reliable. It has been piloted successfully in Guangzhou BRT to demonstrate it as another successful example of parallel transportation systems.

The application of supervised learning to train an intelligent vehicle with a neuro-fuzzy controller to mimic the driving behavior of a human driver is discussed. An initial fuzzy control system for vehicle driving was set up on the basis of general human driving experiences, and its control rules were modified to fit the driving behavior of an individual driver. This provides an effective mechanism to construct driving control systems with personality for automated intelligent vehicles.

This paper proposes a novel approach to constructing fuzzy logic control system using the combination of wavelet network and feedforward BP networks. Numerous works have been reported on the integration, of fuzzy logic systems and neural networks; however most of these hybridizations have resulted in the loss of the original knowledge structure embedded in its network implementation. Due to the inherent modularity, the decision-making process of a fuzzy logic control system can be readily partitioned into three functionally independent modules, namely pattern recognition, fuzzy reasoning and control synthesis, and therefore individual subnets can be built to implement these modules as subnets for the overall feedforward networks. Both wavelet network and BP feedforward network are adopted for the construction of the subnets so that the local receptive field-type wavelet networks with orthogonal least square learning algorithm are capable to capture the fine details of the underlying dynamics, and the overall feedforward structure with the BP algorithm is responsible for the global optimization of the networks parameters. Simulation also shows its superiority.

The proposed system architecture presents a mobile-agent based three-tier distributed control architectural framework in order to provide QoS-driven, scalable system configuration, flexible integration of heterogeneous control systems and robust hierarchical distributed control system for home automation and intelligent control. In this architecture, CORBA and mobile agent technologies work together as a flexible middle layer to provide the distribution of service control and location-aware adaptability and achieve large accessibility and interoperability for distributed control. To satisfy real-time requirements of home automation, the priority queue scheduling, real time event dispatching mechanisms and HAN architecture are also presented.

This paper outlines the architecture and state of art technology of utilizing computer communication networks and field programmable logic devices to implement intelligent neuro-fuzzy control systems for smart consumer electronics.