Control theory in control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner without delay or overshoot and ensuring control stability.
Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn 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 knowledge 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.
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization. This separation is done using the nearest hyperplane that can be written down instantaneously. In the two most important implementations the neighborhood of generalization either varies with the training sample or remains constant. These networks use unary coding for an effective representation of the data sets.
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms.
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from recurrent neural networks.
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
Central pattern generators (CPGs) are biological neural circuits that produce rhythmic outputs in the absence of rhythmic input. They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, flying, ejaculating, urinating, defecating, breathing, or chewing. The ability to function without input from higher brain areas still requires modulatory inputs, and their outputs are not fixed. Flexibility in response to sensory input is a fundamental quality of CPG-driven behavior. To be classified as a rhythmic generator, a CPG requires:
- "two or more processes that interact such that each process sequentially increases and decreases, and
- that, as a result of this interaction, the system repeatedly returns to its starting condition."
Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models, and the other one searching for potential quantum effects in the brain.
The terms closed system and open system have long been defined in the widely established subject of thermodynamics, in terms that have nothing to do with the concepts of feedback and feedforward. The terms 'feedforward' and 'feedback' arose first in the 1920s in the theory of amplifier design, more recently than the thermodynamic terms. Negative feedback was eventually patented by H.S Black in 1934. In thermodynamics, an open system is one that can take in and give out ponderable matter. In thermodynamics, a closed system is one that cannot take in or give out ponderable matter, but may be able to take in or give out radiation and heat and work or any form of energy. In thermodynamics, a closed system can be further restricted, by being 'isolated': an isolated system cannot take in nor give out either ponderable matter or any form of energy. It does not make sense to try to use these well established terms to try to distinguish the presence or absence of feedback in a control system.
Neurorobotics, a combined study of neuroscience, robotics, and artificial intelligence, is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms, computational models of biological neural networks and actual biological systems. Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures.
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.
A Bayesian Confidence Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem: node activations represent probability ("confidence") in the presence of input features or categories, synaptic weights are based on estimated correlations and the spread of activation corresponds to calculating posteriori probabilities. It was originally proposed by Anders Lansner and Örjan Ekeberg at KTH.
Neurocomputational speech processing is computer-simulation of speech production and speech perception by referring to the natural neuronal processes of speech production and speech perception, as they occur in the human nervous system. This topic is based on neuroscience and computational neuroscience.
AnimatLab is an open-source neuromechanical simulation tool that allows authors to easily build and test biomechanical models and the neural networks that control them to produce behaviors. Users can construct neural models of varied level of detail, 3D mechanical models of triangle meshes, and use muscles, motors, receptive fields, stretch sensors, and other transducers to interface the two systems. Experiments can be run in which various stimuli are applied and data is recorded, making it a useful tool for computational neuroscience. The software can also be used to model biomimetic robotic systems.
