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Cellular neural network with trapezoidal activation function

https://doi.org/10.1002/cta.328
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1097-007x(199811/12)26:6<567::aid-cta34>3.0.co;2-topenalex-snapshot
A unified framework for multilayer high order CNN
inferredcites →https://doi.org/10.1002/cta.182openalex-snapshot
Absolutely exponential stability in delayed cellular neural networks
inferredcites →https://doi.org/10.1002/cta.206openalex-snapshot
Novel stability criteria for bidirectional associative memory neural networks with time delays
inferredcites →https://doi.org/10.1002/cta.4490210102openalex-snapshot
Stability analysis of generalized cellular neural networks
inferredcites →https://doi.org/10.1002/cta.4490210505openalex-snapshot
On the dynamic behaviour of two‐cell cellular neural networks
inferredcites →https://doi.org/10.1002/cta.4490220202openalex-snapshot
Some dynamic phenomena in delayed cellular neural networks
inferredcites →https://doi.org/10.1006/cviu.1997.0646openalex-snapshot
Texture Classification and Segmentation by Cellular Neural Networks Using Genetic Learning
inferredcites →https://doi.org/10.1017/cbo9780511754494openalex-snapshot
Cellular Neural Networks and Visual Computing
inferredcites →https://doi.org/10.1109/31.7600openalex-snapshot
Cellular neural networks: theory
inferredcites →https://doi.org/10.1109/31.7601openalex-snapshot
Cellular neural networks: applications
inferredcites →https://doi.org/10.1109/92.678895openalex-snapshot
Time multiplexed color image processing based on a CNN with cell-state outputs
inferredcites →https://doi.org/10.1117/1.482625openalex-snapshot
Cellular neural network architecture for Gibbs random field-based image segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cta.481openalex-snapshot
Single‐layer perceptron and dynamic neuron implementing linearly non‐separable Boolean functions
inferred← citeshttps://doi.org/10.1007/978-3-031-99557-6_19openalex-snapshot
Bessel-Type Activation Functions in the Classification of Medical Images
inferred← citeshttps://doi.org/10.1007/978-3-540-72432-2_72openalex-snapshot
The Game of Life Using Polynomial Discrete Time Cellular Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-540-74690-4_93openalex-snapshot
Polynomial Cellular Neural Networks for Implementing the Game of Life
inferred← citeshttps://doi.org/10.1007/978-3-642-04516-5_11openalex-snapshot
Discovering Universal Polynomial Cellular Neural Networks through Genetic Algorithms
inferred← citeshttps://doi.org/10.1007/s10489-021-03054-2openalex-snapshot
Diversified feature representation via deep auto-encoder ensemble through multiple activation functions
inferred← citeshttps://doi.org/10.1016/j.procs.2025.07.123openalex-snapshot
Bessel-type Activation Functions in Neural Networks
inferred← citeshttps://doi.org/10.1080/00207217.2015.1036811openalex-snapshot
Application of neural networks with orthogonal activation functions in control of dynamical systems
inferred← citeshttps://doi.org/10.1109/cnna.2006.341598openalex-snapshot
Polynomial Discrete Time Cellular Neural Networks to solve the XOR problem
inferred← citeshttps://doi.org/10.1109/icecs.2006.379783openalex-snapshot
CMOS Realization of a Quantized-Output Classifier Circuit
inferred← citeshttps://doi.org/10.1109/newcas.2009.5290438openalex-snapshot
Linearly weighted classifier circuit
inferred← citeshttps://doi.org/10.1109/tnn.2007.902961openalex-snapshot
A CMOS Classifier Circuit Using Neural Networks With Novel Architecture
inferred← citeshttps://doi.org/10.3390/math13142232openalex-snapshot
Hypergeometric Functions as Activation Functions: The Particular Case of Bessel-Type Functions
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