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Figure 2 | Journal of NeuroEngineering and Rehabilitation

Figure 2

From: A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants

Figure 2

Layer structure of the model. Most of the neurons in the input layer detect the occurrence of events and mapping the events into fuzzy variables. Others are pre-processing neurons for certain types of inputs, such as performing as pharmacokinetic models to map the dose and/or regimen of one kind of medication into the effective concentration, or integration neurons to calculate the accumulative effect of interventions. For each state, there are generally five nuclei in the rule-state layer. The outputs of tonic rules nuclei determine the absolute value of the state, and the phasic rules nuclei brings the instant change to the state. (Specially, the nuclei connect the fact/context and the states as tonic rules and phasic rules, with neuronal leaky integrators defined by a time constant to describe how fast the caused change in states reaches its result value.) One nuclei functions as homeostasis mechanism, whose reference is given by the output of phasic rule for reference nuclei (see also Figure 3). The last nuclei works as a math model to relate the Type B interventions and the change of the state. The output of the integration neuron in the rule-state layer is the state X, which then along with inputs are mapped into output Y. The outcome J is a function of all inputs, states, and outputs.

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