Abstract
BP algorithm is a typical artificial neural network learning algorithm, the main structure consists of an input layer, one or more hidden layer, an output layer, the layers of the number of neurons, the output of each node the value is decided by the input values, the role, function and threshold. The Internet of Things is based on the information carrier of the traditional telecommunications network, so that all can be individually addressable ordinary physical objects to achieve the interoperability network. The paper puts forward the application of BP neural network in internet of things. The experiment shows BP is superior to RFID in internet of things.
Highlights
IntroductionArtificial neural network (based Artificial Neural Network, referred to as ANN) system is to learn from some of the features in the human brain and nervous system to store and process information abstracted from the digital model of an artificial intelligence, with a parallel distributed information processing structure, through the nonlinear function of the compound to approximate the mapping between the input and output
Artificial neural network system is to learn from some of the features in the human brain and nervous system to store and process information abstracted from the digital model of an artificial intelligence, with a parallel distributed information processing structure, through the nonlinear function of the compound to approximate the mapping between the input and output
Artificial neural network itself contains a lot of network models such as BP neural network, Hopfield network, the Hamming network
Summary
Artificial neural network (based Artificial Neural Network, referred to as ANN) system is to learn from some of the features in the human brain and nervous system to store and process information abstracted from the digital model of an artificial intelligence, with a parallel distributed information processing structure, through the nonlinear function of the compound to approximate the mapping between the input and output. Grossberg networks and competitive network, arbitrary precision BP network can approximate any continuous function, so the evaluation of the research literature in the existing competitive BP (Back Propagation network) network mostly Such as Gao Xiaohong, Guo Jun, Wu Xiaowei (2004) to establish the timing of BP neural network and causal BP neural network prediction of the competitiveness of enterprises, according to the index system of enterprise competitiveness; Li Yuhua (2006) by the BP network on the core competitiveness of the old industrial bases the force was evaluated; Chen transferred (2003) by BP neural network was evaluated on bank competitiveness. The Internet of Things is based on the information carrier of the Internet, the traditional telecommunications network, so that all can be individually addressable ordinary physical The paper puts forward the application of BP neural network in internet of things
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