Earticle

초록

영어

Neurocomputing in complex domain has yielded second generation neural networks. The neural network, which is based on complex value, contains different layers. The attributes of these layers are biases, weights, inputs and outputs. These attributes are also complex numbers. The signal processing, speech processing, learning and prediction of motion on plane are few areas in which complex domain neurocomputing is applied., since in the above said areas, the inputs and outputs are represented by complex values. It has been observed that the neural network with complex value can easily perform the transformation of geometric figures. The examples of transformations are rotation, parallel displacement of straight lines and circles. The neural network can extend to complex domain by the application of transformation. A number in complex domain is composed of different entities i.e. two real numbers and phase information. The two real numbers and phase information of any point on plane is naturally embedded in this number.

목차

Abstact
 1. Introduction
 2. Neural Network with Complex Valued
  2.1. The Behavior of Algorithm of Learning in Complex Domain
 3. Geometrical Transformations
  3.1. Rotation Transformation
  3.2. Similarity Transformation
 4. Conclusions
 References

키워드

저자

• Manmohan Shukla [ Associate Professor CSE Dept. MPEC Kanpur, Professor CSE Dept. HBTI Kanpur ]
• B. K. Tripathi [ Associate Professor CSE Dept. MPEC Kanpur, Professor CSE Dept. HBTI Kanpur ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605