A neural net capable of restoring continuous level library vectors from memory is considered. As with Hopfield's neural net content addressable memory, the vectors in the memory library are used to program the neural interconnects. Given a portion of one of the library vectors, the net extrapolates the remainder. Sufficient conditions for convergence are stated. Effects of processor inexactitud...

Gliomas are the largest prevalent and destructive of brain tumors have crucial parts for diagnosing treating MRI during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive tumor segmentation, but this role remains challenging due to differing severity appearance gliomas. Therefore, we proposed a novel encoder–decoder called Multi Inception Residual Atte...

In this paper, we explore the application of Recursive Neural Networks on the sentiment analysis task with tweets. Tweets, being a form of communication that has been largely infused with symbols and short-hands, are especially challenging as a sentiment analysis task. In this project, we experiment with different genres of neural net and analyze how models suit the data set in which the nature...

This paper presents a survey of neural network controllers for AUVs. A direct adaptive neural net controller incorporating integral action was designed for the heave motion of the ODIN underwater vehicle. The neural net controller is trained on-line by parallel recursive error prediction method and the critic equation. The influence of the various design parameters of the neural net controller ...

This paper presents the potential of the herein so-called neural net filters in communication systems. The use of integrated signaling and coding techniques in modern modulation schemes has not a right correspondence in the same digital signal processing integrated tools. The neural net filter seems to be the needed contribution. Introduced from de basic neural structure associated with coding ...

This paper introduces TiPo, a new neural net model with superior evolvabilities than all previously known neural net models for dynamic functions. TiPo neural nets can dynamically change their structure with each clock tick. This provides enhanced computability for highly dynamic functions, such as curve following. Our goal is to implement this model in a secondgeneration brain-building machine...