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Programmed optoelectronic time-pulse coded relational processor as base element for sorting neural networks

机译:编程的光电时间脉冲编码关系处理器作为神经网络分类的基础元素

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In the paper we show that the biologically motivated conception of the use of time-pulse encoding gives the row of advantages (single methodological basis, universality, simplicity of tuning, training and programming et al) at creation and designing of sensor systems with parallel input-output and processing, 2D-structures of hybrid and neuro-fuzzy neurocomputers of next generations. We show principles of construction of programmable relational optoelectronic time-pulse coded processors, continuous logic, order logic and temporal waves processes, that lie in basis of the creation. We consider structure that executes extraction of analog signal of the set grade (order), sorting of analog and time-pulse coded variables. We offer optoelectronic realization of such base relational elements of order logic, which consists of time-pulse coded phototransformers (pulse-width and pulse-phase modulators) with direct and complementary outputs, sorting network on logical elements and programmable commutations blocks. We make estimations of basic technical parameters of such base devices and processors on their basis by simulation and experimental research: power of optical input signals - 0.200-20 μW, processing time - microseconds, supply voltage -1.5-10 V, consumption power - hundreds of microwatts per element, extended functional possibilities, training possibilities. We discuss some aspects of possible rules and principles of training and programmable tuning on the required function, relational operation and realization of hardware blocks for modifications of such processors. We show as on the basis of such quasiuniversal hardware simple block and flexible programmable tuning it is possible to create sorting machines, neural networks and hybrid data-processing systems with the untraditional numerical systems and pictures operands.
机译:在本文中,我们证明了在创建和设计具有并行输入的传感器系统时,出于时间考虑,使用时间脉冲编码具有生物学动机,因此具有一系列优势(单一方法论基础,通用性,调试,编程和编程等方面的优势) -输出和处理,下一代混合和神经模糊神经计算机的2D结构。我们展示了基于关系的可编程关系光电时间脉冲编码处理器,连续逻辑,阶逻辑和时间波过程的​​构造原理。我们考虑执行设定等级(顺序)的模拟信号提取,模拟和时间脉冲编码变量的分类的结构。我们提供此类阶逻辑基本关系元件的光电实现,该元件由具有直接和互补输出的时间脉冲编码光电变压器(脉宽和脉冲相位调制器),逻辑元件上的排序网络和可编程换向块组成。我们通过仿真和实验研究,基于这些基础设备和处理器的基本技术参数进行估算:光输入信号的功率-0.200-20μW,处理时间-微秒,电源电压-1.5-10 V,功耗-数百每个元素的微瓦数,扩展的功能可能性,培训的可能性。我们讨论了可能的规则和原则的某些方面,其中包括对所需功能的培训和可编程调整,关系操作以及对此类处理器进行修改的硬件模块的实现。我们证明,基于此类准通用硬件的简单块和灵活的可编程调整,可以创建具有非传统数值系统和图片操作数的分类机,神经网络和混合数据处理系统。

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