Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

Cognitive engineering

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

Neuro-fuzzy rule generation: survey in soft computing framework

Fuzzy neural networks: a survey

We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce "fuzzy curves" and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network. >

A new approach to fuzzy-neural system modeling

An architecture of neural networks with interval weights and its application to fuzzy regression analysis

An architecture of multi-layer feedforward neural networks whose weights and biases are given as fuzzy numbers is proposed The fuzzy neural network with the proposed architecture maps an input vector of real numbers to a fuzzy output The input-output relation of each unit is defined by the extension principle A learning algorithm of the fuzzy neural networks is derived for real input vectors and fuzzy target outputs The derived learning algorithm is extended to the case of fuzzy input vectors and fuzzy target outputs >

Fuzzy neural networks with fuzzy weights and fuzzy biases

An apparatus for recording an interaction log includes an event detector for determining whether a reference event occurred in an interactive system. A state detector detects a parameter indicating a state of the interactive system when the event detector determined that the reference event occurred. A recorder records the parameter detected by the state detector. A latching mechanism latches information to be transferred from an input unit to a processing unit so that the processing unit does not respond to the reference event. A judging mechanism releases the information latched by the latching mechanism when the state detector completes detection. Thus, the state detector detects the parameter in synchronization with occurrence of the reference event. The apparatus also may include a plurality of imaging units and a controller. Each of the imaging units generates an image including graphical items corresponding to reference events recorded in the recorder. The controller receives information designating one of the reference events as a selected event, The controller directs the plurality of imaging units to mark a graphical item that corresponds to the selected event.

Method and apparatus for recording and analyzing an interaction log

Interpolation of fuzzy if-then rules by neural networks

A screen of a system is comprehensively shifted by automatically operating a system having a GUI and images on the screen are recorded at respective time instances. Operation objects of the system displayed on the screen is detected by an operation object detection unit (102) and sequentially selected by an operation object selection unit (108) such that the detected operation objects are comprehensively operated. An operation event for executing an operation of the selected operation object is generated by an operation event generator unit (109) and output to a program execution unit (101) for controlling an execution of an execution program of the system. A screen image recording unit (103) acquires the screen image from the program execution unit (101) and records it.

Hidehiko Okada

Papers

An architecture of neural networks with interval weights and its application to fuzzy regression analysis

Fuzzy neural networks with fuzzy weights and fuzzy biases

Method and apparatus for recording and analyzing an interaction log

Interpolation of fuzzy if-then rules by neural networks

Automatic GUI system operation device and operation macro execution device