1. A noise-constrained diffusion least mean square method for estimation in adaptive networks, comprising the steps of: (a) establishing an adaptive network having N nodes, wherein N is an integer greater than one, and for each node k, a number of neighbors of node k is given by N k , including the node k, wherein k is an integer between one and N; (b) establishing an integer i and initially setting i=1; (c) calculating an output of the adaptive network at each node k as d k (i)=u k,i w 0 +v k (i), wherein u k,i represents a known regressor row vector of length M, w 0 represents an unknown column vector of length M and v k (i) represents noise in the adaptive network, wherein M is an integer; (d) calculating an error value e k (i) at each node k as e k (i)=d k (i)−u k,i w k,i-1 , wherein w k,i represents an estimate of an output vector for node k at iteration i; (e) calculating a local estimate for each node neighboring node k, ψ k,i , for each node k as ψ k,i =w k,i-1 +μ k (1+γλ k,i-1 )u k,i *e k (i), wherein λ k , i = ( 1 - β ) λ k , i - 1 + β 2 ( e k , i 2 - σ v , k 2 ) , β and γ being selectable unitless parameters, μ k represents a node step size for each node k, and wherein σ v,k 2 represents an additive noise variance; (f) calculating an estimate of an output vector w k,i for each node k as w k , i = ∑ l ∈ N k c lk ψ l , i , , wherein l is an integer, c lk represents a combination weight for node k; (g) if e k (i) is greater than a selected error threshold, then setting i=i+1 and returning to step (c); otherwise, (h) defining a set of output vectors w k for each node k, where w k =w k,i ; and (i) storing the set of output vectors in computer readable memory.