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In computer science and machine learning, cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs. ==CNN architecture== Due to their number and variety of architectures, it is difficult to give a precise definition for a CNN processor. From an architecture standpoint, CNN processors are a system of a finite, fixed-number, fixed-location, fixed-topology, locally interconnected, multiple-input, single-output, nonlinear processing units. The nonlinear processing units are often referred to as neurons or cells. Mathematically, each cell can be modeled as a dissipative, nonlinear dynamical system where information is encoded via its initial state, inputs and variables used to define its behavior. Dynamics are usually continuous, Continuous-Time CNN (CT-CNN) processors, but can be discrete, Discrete-Time CNN (DT-CNN) processors. Each cell has one output, by which it communicates its state with both other cells and external devices. Output is typically real-valued, but can be complex or even quaternion, i.e. Multi-Valued CNN (MV-CNN). In most CNN processors, processing units are identical, but there are applications that require, Non-Uniform Processor CNN (NUP-CNN) processor, consisting of different types of cells. In the original Chua-Yang CNN (CY-CNN) processor, the state of the cell was a weighted sum of the inputs and the output was a piecewise linear function. However, like the original perceptron-based neural networks, the functions it could perform were limited: specifically, it was incapable of modeling non-linear functions, such as XOR. If this is a problem, more complex functions are achievable via Non-Linear CNN (NL-CNN) processors. Cells are defined in a normed space, commonly a two-dimensional Euclidean geometry, like a grid. The cells are not limited to two-dimensional spaces however; they can be defined in arbitrary numbers of dimensions and can be square, triangle, hexagonal, or any other spatially invariant arrangement. Topologically, cells can be arranged on an infinite plane or on a toroidal space. Cell interconnect is local, meaning that all connections between cells are within a specified radius, where distance is measured topologically. Connections can also be time-delayed to allow for processing in the temporal domain. Most CNN architectures have cells with the same relative interconnect, but there are applications that require, Multiple-Neighborhood-Size CNN (MNS-CNN), consisting of spatially variant topology. Also, Multiple-Layer CNN (ML-CNN), where all cells on the same layer are identical, can be used to extend the capability of CNN processors. The definition of a system is a collection of independent, interacting entities forming an integrated whole, whose behavior is distinct and qualitatively greater than its entities. Although connections are local, information exchange can happen globally through diffusion. In this sense, CNN processors are systems because their dynamics is derived from the interaction between the processing units and not within processing units. As a result, they exhibit emergent and collective behavior. Mathematically, the relationship between a cell and its neighbors, located within an area of influence, can be defined by a coupling law, and this is what primarily determines the behavior of the processor. When the coupling laws are modeled by fuzzy logic, it is fuzzy CNN . When these laws are modeled by computational verb logic, it becomes computational verb CNN (verb CNN) . Both fuzzy and verb CNNs are useful to modelling social networks when the local couplings are achieved by linguistic terms. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Cellular neural network」の詳細全文を読む スポンサード リンク
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