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N
- neural net
- See neural network.
- neural network
- Usually used to mean artificial neural network (ANN). Biological neural networks are much more complicated in their elementary structures than the mathematical models used for ANNs.
An ANN is a network of many very simple processors (units or "neurons"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections.
- neuron
- See neural network, McCulloch-Pitts.
- Newton-Raphson
- A well-known {algorithm} for solving equations. Given an equation,
f (x) = 0
and an initial approximation, x(0), a better approximation is given by:x(i+1) = x(i) - f(x(i)) / f'(x(i))
where f'(x) is the first derivative of f, df/dx.
- nondeterministic
- Exhibiting nondeterminism.
- nondeterministic automaton
- (Or "probabilistic automaton") An automaton in which there are several possible actions (outputs and next states) at each state of the computation such that the overall course of the computation is not completely determined by the program, the starting state, and the initial inputs.
- nondeterministic polynomial time
- (NP) A set or property of computational decision problems solvable by a nondeterministic Turing Machine in a number of steps that is a polynomial function of the size of the input. The word "nondeterministic" suggests a method of generating potential solutions using some form of nondeterminism or "trial and error". This may take exponential time as long as a potential solution can be verified in polynomial time.
- nonlinear
- (Scientific computation) A property of a system whose output is not proportional to its input. The behaviour of a system containing non-linear components is thus harder to model and to predict.
- normal distribution
- (Or "Gaussian distribution", "bell curve") The frequency distribution of many natural phenomena such as the height of people of a certain age and sex. The formula looks something like:
P(x) = e^(((x-m)/s)^2)
where P(x) is the probability of a measurement x, m is the mean value of x and s is the {standard deviation}.
- normalisation
- A transformation applied uniformly to each element in a set of data so that the set has some specific statistical property. For example, monthly measurements of the rainfall in London might be normalised by dividing each one by the total for the year to give a profile of rainfall throughout the year.
- normalised
- Resulting from normalisation.
- NP-complete
- (NPC, Nondeterministic Polynomial time complete) A set or property of computational decision problems which is a subset of {NP} (i.e. can be solved by a nondeterministic Turing Machine in polynomial time), with the additional property that it is also NP-hard. Thus a solution for one NP-complete problem would solve all problems in NP. Many (but not all) naturally arising problems in class NP are in fact NP-complete.
- NP-hard
- A set or property of computational search problems. A problem is NP-hard if solving it in polynomial time would make it possible to solve all problems in class NP in polynomial time. Some NP-hard problems are also in NP (these are called NP-complete), some are not. If you could reduce an NP problem to an NP-hard problem and then solve it in polynomial time, you could solve all NP problems.
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