In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR sig...

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR sig...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

This paper proposes a simple enhanced augmented Hopfield Lagrange neural network (EALH) for solving economic dispatch (ED) problem with piecewise quadratic cost functions. The EALH is an augmented Lagrange Hopfield neural network (ALH), which is a combination of continuous Hopfield neural network and augmented Lagrangian relaxation function as its energy function, enhanced by a heuristic search...

In this letter we unveil the existence of transient hidden coexisting chaotic attractors, in a simplified Hopfield neural network with three neurons. keyword Hopfield neural network; Transient hidden chaotic attractor; Limit cycle

Continuous-time Hopfield network has been an important focus of research area since 1980s whose applications vary from image restoration to combinatorial optimization from control engineering to associative memory systems. On the other hand, in wireless communications systems literature, power control has been intensively studied as an essential mechanism for increasing the system performance. ...

The effect of noise degradation on the Hopfield neural netwerk is s t u d i e d The notion of a hysteresis nefwork is defined. A noisy HephsSi =urd network is subsequently proven to be a hysteresis network. The effect of the hysteresis phentmenon on the robustness of the HoppeM neural network to noise degradation is then investigated. An eptiraal Heefield neural network is defined as the Hopfie...

The combination of evolutionary algorithms and ANN has been a recent interest in the field of research. Hopfield model is a type of recurrent neural network which has been widely studied for the purpose of associative memories. In the present work, this Hopfield Model of feedback neural networks has been studied with Monte Carlo adaptation learning rule and one evolutionary searching algorithm ...