In recent years the multi-cores and General-Purpose GPU (GPGPU) architectures have become general platforms for various of parallel applications, with lots of parallel algorithms being proposed for this interesting persperctive. In this report, we study and develop a particular kind of artificial neural network (ANN), in hopfield model, to solve some optimization problems, since it has a highly...

The restricted Maximum k-Satisfiability MAXkSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Gen...

Identifying corresponding features in an image sequence is an important issue in motion analysis. We present a solution based on the assumption of smooth motion using point features. Local constraints are used to make the method robust against occlusion and imperfect feature extraction. A global cost function is defined which is minimised by a mapping of feature points onto a 2-D Hopfield neura...

It is well known that stability of Hopfield type neural networks plays a very important role in both theoretical research and applications. So, it has been kept on studying in two decades. Stochastic effectiveness to this kind of neural networks has also received a lot of attention (ref. [Liao et al, 1996 A], [Liao et al, 1996 B], [Blythe,S. et al, 2001A] and [Blythe,S. et al, 2001B]). In this ...

The paper deals with a comparison between two different methods in generating sub-optimal solutions to the Hopfield-Tank TSP (traveling salesman problem) neural algorithm. Namely, the Hopfield-Tank TSP neural algorithm has been applied in the paper to the linear ship’s route, that is cycle voyage modeling, which means here finding the optimal visiting order of a given set of ports in such way a...

In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation in the context of system identification. This subject is a very active field of research, because even when a model of a physical system is available, some parameters may be uncertain or time varying. In our methodology, identification is formulated as an optimization problem, profiting from ...

This paper presents a method for extraction and recognition of trucks from aerial photographs. A new method, mean shift image segmentation, is employed to extract truck shadow and boundary information. After a conversion of the boundaries into vector data, an enclosing minimum area rectangle is fit to each boundary. These rectangles are input as candidates into a designed Hopfield Neural Networ...

This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadrat...

Discrete tomography deals with the reconstruction of binary images from their projections in a small number of directions. In this paper we consider possible neural network approaches to this tomographic reconstruction problem. In particular we are interested in methods that can compute reconstructions in real-time and make efficient use of prior knowledge about the images, even when this knowl...