STABILITY OF MULTI-LAYER CELLULAR NEURAL/NONLINEAR NETWORKS
نویسندگان
چکیده
منابع مشابه
Stability of Multi-Layer Cellular Neural/Nonlinear Networks
We have found a formalism that lets us present generalizations of several stability theorems (see Chua & Roska, 1990; Chua & Wu, 1992; Gilli, 1993; Forti, 2002] on Multi-Layer Cellular Neural/Nonlinear Networks (MLCNN) formerly claimed for Single-Layer Cellular Neural/Nonlinear Networks (CNN). The theorems were selected with special regard to usefulness in engineering applications. Hence, in co...
متن کاملPower Control in Multi-Layer Cellular Networks
We investigate the possible performance gains of power control in multi-layer cellular systems where microcells and picocells are distributed within macrocells. Although multilayers in cellular networks help increase system capacity and coverage, and can reduce total energy consumption; they cause interference, reducing the performance of the network. Therefore, downlink transmit power levels o...
متن کاملA Novel Design of a Multi-layer 2:4 Decoder using Quantum- Dot Cellular Automata
The quantum-dot cellular automata (QCA) is considered as an alternative tocomplementary metal oxide semiconductor (CMOS) technology based on physicalphenomena like Coulomb interaction to overcome the physical limitations of thistechnology. The decoder is one of the important components in digital circuits, whichcan be used in more comprehensive circuits such as full adde...
متن کاملCross Layer Provision of Future Cellular Networks
To cope with the growing demand for wireless data and to extend service coverage, future 5G networks will increasingly rely on the use of low powered nodes to support massive connectivity in diverse set of applications and services [1]. To this end, virtualized and mass-scale cloud architectures are proposed as promising technologies for 5G in which all the nodes are connected via a backhaul ne...
متن کاملThe Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)
Optical Coherence Tomography (OCT) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. An inherent characteristic of coherent imaging is the presence of speckle noise. In this study we use a new ensemble framework which is a combination of several Multi-Layer Perceptron (MLP) neural networks to denoise OCT images. The noise is...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Bifurcation and Chaos
سال: 2004
ISSN: 0218-1274,1793-6551
DOI: 10.1142/s0218127404011582