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(ii) Measurements of distance: It is considered as the distance between the hidden node centre (c) and the input parameter vector (x) called the Euclidian distance of measure and it is given as ‖x t) − c j t)‖.
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Figure 2 The hidden node detection scenario.
The variable w j ∈ ℛ m is the link connecting the jth hidden node with the output layer and g is the hidden node activation function.
As introduced above, there is a fundamental trade-off between the exposed and hidden node problems and several MAC-layer techniques have been proposed to tackle it.
In the developed SaE-ELM model, the network hidden node parameters of the ELM are optimized using SaE algorithm.
β j is the output weights of the jth hidden node.
w j and b j are the input weights and bias of the jth hidden node, respectively.
The general mathematical expression of the output nodes in a RBF network with Gaussian activation functions is as follows: where f j (v) is the function corresponding to the j-th output node and is a linear combination of k radial basis functions with center μ i and bandwidth σ i ; w ji is the weight associated with the link between the j-th output node and the i-th hidden node.
If adding a hidden still cannot improve classification performance, the feature picked out at Step 4 is regarded as useless and will be eliminated from the candidate feature array, and the newly added hidden node will be eliminated too.
However, the multi-channel hidden node problem is completely ignored in the design.
By the additional MDA ACK and MDA ADV, the MMDA algorithm can alleviate hidden node problem in wireless mesh LAN.
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