The most promising approaches for optical neural networks are based on intensity encoding. However, a serious drawback of intensity encoding is the lack of negative values and optical subtraction, which are essential for rendering neural networks useful. To overcome the need for optical subtraction, a novel training method is described here that is especially useful for optical multilayer neura...

A major problem in artificial brain building is the automatic construction and training of multi-module systems of neural networks. For example, consider a biological human brain, which has millions of neural nets. If an artificial brain is to have similar complexity, it is unrealistic to require that the training data set for each neural net must be specified explicitly by a human, or that int...

The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of Artificial Intelligence. The current paper focuses on the use of neural network in order to mitigate the problems of digital handwriting recognition by using Self-Organizing Map model for fast processing and less processing ...

Abstract There are problems such as low recognition accuracy and large classification error in the existing methods for ship identification based on optical remote sensing images. In this paper, we will analyze characteristics of ships determine indicative factors applying to monitor combination with Using image data, combined U-Net AttU-Net deep neural network models, assist extracting new ind...

Artificial neural networks are increasingly popular in today’s business fields. They have been hailed as the greatest technological advance since the invention of transistors. The purpose of this paper is to answer hvo of the inost frequently asked questions: “What are neural networks?” “ Why are they so popular in today’s business fields?” The paper reviews the common characteristics of neural...

In this article a new neural network architecture suitable for learning and generalization is discussed and developed. The architecture is inspired and modeled after quantum electronic devices and circuits where coherent electronic wavefunctions traveling through different parts of the circuit are combined together and result in interferences at detection nodes. These wavefunctions, represented...

Traditionally, VLSI implementations of spiking neural nets have featured large neuron counts for fixed computations or small exploratory, configurable nets. This paper presents the system architecture of a large configurable neural net system employing a dedicated mapping algorithm for projecting the targeted biology-analog nets and dynamics onto the hardware with its attendant constraints. Key...

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificiaa intelligence presents major problems as well. We claim that a promising way out of this impas...

In this paper an hybrid system and a hierarchical neural net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of non-neural clustering algorithms directly to the output of a neural net; the second one is based on a multi-layer organization of neural units. Both methods are a substantial improvement with res...