Browsing by Subject "Cellular neural networks"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Analog CMOS implementation of cellular neural networks
    (IEEE, 1993) Baktır, I. A.; Tan, M. A.
    The analog CMOS circuit realization of cellular neural networks with transconductance elements is presented. This realization can be easily adapted to various types of applications in image processing just by choosing the appropriate transconductance parameters according to the predetermined coefficients. The effectiveness of the designed circuits for connected component detection is shown by HSPICE simulations. For “fixed function” cellular neural network circuits the number of transistors are reduced further by using multi-input transconductance elements.
  • Thumbnail Image
    ItemOpen Access
    Design and stability of Hopfield associative memory
    (1991) Savran, M. Erkan
    This thesis is concerned with the selection of connection weights of Hopfield neural network model so that the network functions as a content addressable memory (CAM). We deal with both the discrete and the continuous-time versions of the model using hard-limiter and sigmoid type nonlinearities in the neuron outputs. The analysis can be employed if any other invertible nonlinearity is used. The general characterization of connection weights for fixed-point programming and a condition for asymptotic stability of these fixed points are presented. The general form of connection weights is then inserted in the condition to obtain a design rule. The characterization procedure is also employed for discrete-time cellular neural networks.
  • Thumbnail Image
    ItemOpen Access
    Solving maximum clique problem by cellular neural network
    (The Institution of Engineering and Technology, 1998-07-23) Şengör, N. S.; Yalçın, M. E.; Çakır, Y.; Üçer, M.; Güzeliş, C.; Pekergin, F.; Morgül, Ö.
    An approximate solution of an NP-hard graph theoretical problem, namely finding maximum clique, is presented using cellular neural networks. Like the existing energy descent optimising dynamics, the maximal cliques will be the stable states of cellular neural networks. To illustrate the performance of the method, the results will be compared with those of continuous Hopfield dynamics.