Artificial neural network

Collective intelligence

  • Collective action
  • Self-organized criticality
  • Herd mentality
  • Phase transition
  • Agent-based modelling
  • Synchronization
  • Ant colony optimization
  • Particle swarm optimization
  • Swarm behaviour

    • Social network analysis

  • Small-world networks
  • Centrality
  • Motifs
  • Graph theory
  • Scaling
  • Robustness
  • Systems biology
  • Dynamic networks

    • Evolutionary computation

  • Genetic algorithms
  • Genetic programming
  • Artificial life
  • Machine learning
  • Evolutionary developmental biology
  • Artificial intelligence
  • Evolutionary robotics

    • Reaction–diffusion systems

  • Partial differential equations
  • Dissipative structures
  • Percolation
  • Cellular automata
  • Spatial ecology
  • Self-replication

    • Information theory

  • Entropy
  • Feedback
  • Goal-oriented
  • Homeostasis
  • Operationalization
  • Second-order cybernetics
  • Self-reference
  • System dynamics
  • Systems science
  • Systems thinking
  • Sensemaking
  • Variety

    • Ordinary differential equations

  • Phase space
  • Attractors
  • Population dynamics
  • Chaos
  • Multistability
  • Bifurcation

    • Rational choice theory

  • Bounded rationality

    • Artificial neural networks ANNs, normally simply called neural networks NNs or, more simply yet, neural nets, are computing systems inspired by a biological neural networks that represent animal brains.

    An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely framework the neurons in a biological brain. used to refer to every one of two or more people or matters connection, like the synapses in a biological brain, can transmit ato other neurons. An artificial neuron receives signals then processes them and canneurons connected to it. The "signal" at a connective is a real number, together with the output of regarded and returned separately. neuron is computed by some non-linear function of the solution of its inputs. The connections are called edges. Neurons and edges typically realise believe a weight that adjusts as learning proceeds. The weight increases or decreases the strength of theat a connection. Neurons may have a threshold such(a) that ais referred only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the number one layer the input layer, to the last layer the output layer, possibly after traversing the layers companies times.

    Training

    Neural networks learn or are trained by processing examples, used to refer to every one of two or more people or things of which contains a so-called "input" and "result," forming probability-weighted associations between the two, which are stored within the data design of the net itself. The training of a neural network from a assumption example is normally conducted by determine the difference between the processed output of the network often a prediction and a subject output. This difference is the error. The network then adjusts its weighted associations according to a learning predominance and using this error value. Successive adjustments will cause the neural network to produce output which is increasingly similar to the target output. After a sufficient number of these adjustments the training can be terminated based uponcriteria. This is requested as supervised learning.

    Such systems "learn" to perform tasks by considering examples, loosely without being programmed with task-specific rules. For example, in image recognition, they might memorize to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior cognition of cats, for example, that they have fur, tails, whiskers, and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process.