"Fuzzy Logic is basically a multivalued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, black/white, etc. Notions like rather warm or pretty cold can be formulated mathematically and processed by computers."
– Peter Bauer, Stephan Nouak, and Roman Winkler. A Brief Course in Fuzzy Logic and Fuzzy Control. Available from ESRU [Energy Systems Research Unit], Department of Mechanical Engineering, University of Strathclyde. 1996.
Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.
Inference Engine It accepts and promotes human interpretation by making fuzzy inference according to inputs and IF-THEN rules. A number of other concepts are associated with fuzzy logic such as fuzzy set theory, fuzzy modelling, the fuzzy control system that have been developed for further enhancement. In control systems theory, if the fuzzy interpretation of the problem is appropriate and if the fuzzy theory is developed precise and correct, then fuzzy controllers can be accordingly designed and they work quite well to their advantages. Most of the fuzzy logic control systems are knowledge-based systems which mean either their fuzzy models or their fuzzy logic controllers are described by fuzzy logic IF-THEN rules.
In recent years, the term'machine learning' has become very popular among developers and business alike, even though research in the field has been going on for decades. Essentially, machine learning is about teaching machines to learn concepts and techniques the way humans do. While computer scientists were making huge strides in increasing computational performance by utilizing advancements in hardware to enable machines to solve complex calculations, hypotheses by their fellow researchers from AI on the ability of machines to think and act like humans were met with skepticism. A sub-field of AI, machine learning, saw rapid growth when companies such as Google and Facebook began to find new ways to utilize the troves of data for more profit.
We present CGO-AS, a generalized Ant System (AS) implemented in the framework of Cooperative Group Optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning.
David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called "genetic fuzzy trees" to predict how bipolar patients would respond to lithium. The study authors found that even the best of eight common models used in treating bipolar disorder predicted who would respond to lithium treatment with 75 percent accuracy. But the model UC researchers developed using AI predicted how patients would respond to lithium treatment with 88 percent accuracy and 80 percent accuracy in validation. It turns out that the same kind of artificial intelligence that outmaneuvered Air Force pilots last year in simulation after simulation at Wright-Patterson Air Force Base is equally adept at making beneficial decisions that can help doctors treat disease.
When using fuzzy logic, people's qualitative description as well as quantitative estimation can be elaborated to maximise its utility. This is extremely crucial for data science and actuarial modeling because they can occasionally face lack of data, face time sensitive data and need sound qualitative inputs to profile the complexity of the emerging situation. This is not to say that fuzzy logic systems are not without their shortcomings. In the application of fuzzy logic systems to risk assessment and risk decision-making, many practical issues and challenges can be encountered.
The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. So it's likely that future air combat, requiring reaction times that surpass human capabilities, will integrate AI wingmen - Unmanned Combat Aerial Vehicles (UCAVs) - capable of performing air combat and teamed with manned aircraft wherein an onboard battle management system would be able to process situational awareness, determine reactions, select tactics, manage weapons use and more. It would normally be expected that an artificial intelligence with the learning and performance capabilities of ALPHA, applicable to incredibly complex problems, would require a super computer in order to operate. They tackled the problem using language-based control (vs. States UC's Cohen, "Genetic fuzzy systems have been shown to have high performance, and a problem with four or five inputs can be solved handily.
Common techniques include the Taylor series and the Fourier series approximations. In its most basic form, a one layer neural network with $latex $n input nodes and one output node is described by, where is the input, is the bias and is the weight matrix. In the output layer we have a new weight matrix and bias term applied to . I used Torch to generate the training set and the neural networks.
Artificial Intelligence (AI) may be regarded as an attempt to understand the processes of perception and reasoning that underlie successful problem solving and to incorporate the result of this research in effective computer programs. At present, AI is largely a collection of sophisticated programming technique that seek to develop systems that attempt to mimic human intelligence without claiming an understanding of the underlying processes involved. Artificial Intelligence (AI) can offer may advantages over traditional methods, such as statistical analysis, particularly where the data exhibits some form of non-linearity. Some existing application of spatial analysis and modeling techniques includes artificial neural networks and rule-based system fuzzy logic .