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首页> 外文期刊>Instrumentation and Measurement, IEEE Transactions on >Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks
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Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks

机译：基于Laguerre神经网络的无线传感器网络智能传感器的开发

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摘要

The node of a wireless sensor network (WSN), which contains a sensor module with one or more physical sensors, may be exposed to widely varying environmental conditions, e.g., temperature, pressure, humidity, etc. Most of the sensor response characteristics are nonlinear, and in addition to that, other environmental parameters influence the sensor output nonlinearly. Therefore, to obtain accurate information from the sensors, it is important to linearize the sensor response and compensate for the undesirable environmental influences. In this paper, we present an intelligent technique using a novel computationally efficient Laguerre neural network (LaNN) to compensate for the inherent sensor nonlinearity and the environmental influences. Using the example of a capacitive pressure sensor, we have shown through extensive computer simulations that the proposed LaNN-based sensor can provide highly linearized output, such that the maximum full-scale error remains within $pm$ 1.0% over a wide temperature range from $-$50 $^{circ}hbox{C}$ to 200 $^{ circ}hbox{C}$ for three different types of nonlinear dependences. We have carried out its performance comparison with a multilayer-perceptron-based sensor model. We have also proposed a reduced-complexity run-time implementation scheme for the LaNN-based sensor model, which can save about 50% of the hardware and reduce the execution time by four times, thus making it suitable for the energy-constrained WSN applications.
机译：无线传感器网络（WSN）的节点可能包含暴露于广泛变化的环境条件（例如温度，压力，湿度等），其中该传感器模块包含带有一个或多个物理传感器的传感器模块。大多数传感器响应特性都是非线性的，除此之外，其他环境参数也会非线性影响传感器输出。因此，为了从传感器获得准确的信息，重要的是使传感器响应线性化并补偿不良的环境影响。在本文中，我们提出了一种智能技术，该技术使用新颖的计算效率高的Laguerre神经网络（LaNN）来补偿固有的传感器非线性和环境影响。以电容式压力传感器为例，我们通过广泛的计算机仿真显示，所提出的基于LaNN的传感器可以提供高度线性化的输出，因此，在从... ...的宽温度范围内，最大满量程误差保持在$ pm $ 1.0％以内。 $-$ 50 $ ^ {circ} hbox {C} $到200 $ ^ {circhhbox {C} $）用于三种不同类型的非线性相关性。我们已经将其性能与基于多层感知器的传感器模型进行了比较。我们还针对基于LaNN的传感器模型提出了一种降低复杂性的运行时实现方案，该方案可以节省大约50％的硬件，并减少四倍的执行时间，从而使其适合于能量受限的WSN应用。

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