"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.
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.
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 .
The difference or error signal actuates the controller to generate a control action signal. When the control signal is proportional to the error signal at a given moment, the output can take on any value between zero and one (fully off and fully on). If the control signal varies as the cumulative value of the error signals up to that moment, the control action is integral. Whenever, fuzzy logic is made applicable in these PID control systems, the results are better as now an experienced human operator-like fuzzy rule-based system takes over the control from the water-tight binary logic.
An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.