Building upon the mean$p$-power error (MPE) criterion, the normalized subband$p$-norm (NSPN) algorithm demonstrates superior robustness in$\alpha$-stable noise environments ($1< \alpha \leq 2$) through effective utilization of low-order moment hidden in robust loss functions. Nevertheless, its performance degrades significantly when processing noise input or additive noise characterized by$\alpha$-stable processes ($0< \alpha \leq 1$). To overcome these limitations, we propose a novel fractional-order NSPN (FoNSPN) algorithm that incorporates the fractional-order stochastic gradient descent (FoSGD) method into the MPE framework. Additionally, this paper also analyzes the convergence range of its step-size, the theoretical domain of values for the fractional-order$\beta$, and establishes the theoretical steady-state mean square deviation (MSD) model. Simulations conducted in diverse impulsive noise environments confirm the superiority of the proposed FoNSPN algorithm against existing state-of-the-art algorithms.

Compared with the functional link neural network (FLNN) algorithm, the delayless multi-sampled multiband-structured subband FLNN (DMSFLNN) algorithm provides fast convergence when encountering highly auto-correlated input signals, but there is a compromise between convergence and steady-state performances. Therefore, in order to overcome this flaw, we develop an optimal DMSFLNN (ODMSFLNN) algorithm by minimizing the mean square deviation of the weight vector with respect to the subband gain vectors. Interestingly, a vectorized version is also proposed for the ODMSFLNN algorithm, which aims at reducing computational complexity. Additionally, this paper also presents a stability analysis of this algorithm. Then, considering the impulsive noise environment, we develop two robust variants of ODMSFLNN that are the R-ODMSFLNN-I and R-ODMSFLNN-II algorithms, which are based on the specified robust function and the energy constraint of the weight update increment, respectively. Finally, to resolve that the DMSFLNN algorithm may not exploit cross-terms of input samples in nonlinear active noise control scenarios, we further propose the subband second-order Volterra filter (SSOVF) framework in an analogy way and apply the R-ODMSFLNN-II learning principle to obtain the robust optimal SSOVF algorithm. Simulations in several nonlinear scenarios have shown that the proposed algorithms perform better than their competitors.

For crowd cognitive-based routing behaviors, human vision is a key factor and should be properly evaluated. To model pedestrians’ vision, the paper proposes the mechanism of trap. A trap is merely a tuple of two elements and defines an operating scope and an operation. A trap’s operation will be executed for a person whenever the person is walking inside its scope and caught by it. Implementation issues are discussed and an efficient implementation is sought for the developed OpenCL-based simulation program. By letting traps’ operation to be “seeing certain rooms, doors, etc” and installing them at suitable places, human vision can be modeled conveniently and efficiently. To validate the proposed trap mechanism to some extent, the numerical experiments firstly investigate plausibility of individuals’ evacuation route. Then routing at the collective level is experimented by assuming pedestrians of different levels of map knowledge and vision.

As for a pipe-line plant subjected to complex logical control specifications, an approach is proposed to generate the control codes via Petri net such that the plant is run as concurrently as possible. A PN model, called the plant net, is designed to represent the whole process of a pipe-line system. In this plant net, the tasks, including fluid transportation from one tank to another and cleaning tank, are represented by operational places, and level sensors in tanks are modeled by labels assigned with transitions. Further, conflict structures are used to describe the conflict relations among tasks due to the shared valves and pipes. By introducing a priority order in the transitions, an algorithm is presented to compute the control commands for valves based on this plant net. A beer filtration plant is taken as an example to illustrate the method.

Control of a large-scale automated manufacturing system is an important and challenging issue. Its discrete event system model represented by Petri nets tends to become highly complicated in structure, especially when there exist uncontrollable or unobservable events. The existing approaches are nontrivial to design both efficient and maximally permissive supervisors to impose constraints on an overall system. In this paper, instead of considering the control problem from an overall system perspective, we intend to transform an overall control problem into the one designing multiple controllers in parallel, each of which is much simpler in structure. A Petri net structure is decomposed via integer linear programming or a polynomial decomposition method to obtain multiple state-machine subnets that constitute a decentralized system. A necessary and sufficient condition for preserving the equivalence in terms of states and behaviors between the overall system and its decentralized version is reported. Constraints representing control requirements are further converted and enforced in the respective subnets. Then, supervisors are generated via a generalized mutual exclusion constraint method. By considering the deviations between the subnet control and overall control, this paper formulates a communication mechanism to guarantee that the decentralized system runs in an appropriate manner. Finally, two examples are presented to demonstrate the proposed approach.

This paper develops a decentralized supervision policy for a Petri net through collaboration between a coordinator and subnet controllers. The coordinator is chosen from the subnet controllers by solving an integer linear programming problem. An optimal objective function is used to minimize the communication cost between the subnet controllers and the coordinator. Furthermore, a protocol to reach an agreement on the firing conditions of common transitions among the subnet controllers is proposed. Observation agreement and control agreement can be achieved by the “AND” operator in logic algebra. Control agreement is used to decide the firing conditions of common transitions in the next step. The firing of common transitions, which will lead to a new marking that violates the given constraints, will be forbidden by the control agreement. A feasibility analysis of the proposed decentralized control framework is discussed. Finally, four examples are presented to illustrate the proposed approach.

We have developed an inexpensive yet versatile microcomputer-based system for quantitating light intensity levels in autoradiographs. This system employs a standard video camera interfaced to an analog-to-digital convertor. A program has been written for this system which can measure intensities within a defined region of an autoradiograph, permitting an easy and accurate quantitation of spots or bands of irregular shape.