Radio fuze needs to detect exactly target signal from the echo signal being polluted by noise in real time. Traditional interference cancellation system cannot meet the needs. The Hopfield neural network not only has the ability of nonlinear mapping but also has the ability of selflearning. So it can be used to possess a desired result against the effect of uncertainties and incomplete informat...

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-states of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a long-standing open problem whether robust exp...

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-points of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a longstanding open problem whether robust expo...

The original Hopfield neural networks model is adapted so that the weights of the resulting network are time varying. In this paper, the Discrete Hopfield neural networks with weight function matrix DHNNWFM the weight changes with time, are considered, and the stability of DHNNWFM is analyzed. Combined with the Lyapunov function, we obtain some important results that if weight function matrix W...

The consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance. It is concluded that these networks maintain a reasonable leve...

We introduce a non-instantaneous impulsive Hopfield neural network model in this paper. Firstly, we prove the existence and uniqueness of an almost periodic solution model. Secondly, that is exponentially stable. Finally, give example

Satellite communication systems suffer from the systematic error of tropospheric delay. Accurate estimation this delay is essential for budget and planning. This study investigates in three Nigeria cities: Abuja, Lagos, Port Harcourt using two different models (Saastominen Hopfield). Three-year atmospheric data surface pressure, relative humidity temperature obtained at 5-min interval were acqu...