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This paper takes on the central heating secondary network and establishes a two-level diagnosis model of leakage fault of heating pipe network based on deep belief network (DBN) under the condition of constant and small supply flow quality regulation. Firstly, a leakage condition hydraulic calculation model of the heating pipe network is established with graph theory, which provides the pressure changes of the pressure monitoring points in the pipe network. Then, the first-level diagnostic model for the leakage of the heating pipe network is designed to diagnose the leaky pipe segment by using a deep belief network. Based on the results of the first-level diagnostic model, each leaky pipe segment is treated as a unit and a second-level diagnosis model is then developed to predict the specific leak location. Finally, the model is verified with a branch-pipe network and a loop-pipe network. Experimental results showed that the firstlevel diagnostic model had a high accuracy rate in the prediction of leaky pipe segments, which was superior to traditional fault diagnosis methods such as BP (Back Propagation Neural Network) and SVM (Support Vector Machines). The second-level diagnostic model can detect the leak location of the leaky pipe with satisfactory results.

Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model.

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