Neural network learning and expert systems
Neural network learning and expert systems
Expert systems applications in engineering and manufacturing
Expert systems applications in engineering and manufacturing
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
On the equivalence of neural nets and fuzzy expert systems
Fuzzy Sets and Systems
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Fuzzy MLP based expert system for medical diagnosis
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Knowledge-based artificial neural networks
Artificial Intelligence
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
A partial order for the M-of-N rule-extraction algorithm
IEEE Transactions on Neural Networks
Learning inexpensive parametric design models using an augmented genetic programming technique
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Rule based fuzzy classification using squashing functions
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Soft Computing and Applications
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
ACS'08 Proceedings of the 8th conference on Applied computer scince
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks
Expert Systems with Applications: An International Journal
Simple model-free controller for the stabilization of planetary inverted pendulum
Journal of Control Science and Engineering
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The advent of artificial neural networks has contributed significantly to the field of knowledge engineering. Neural networks belong to a family of models that are based on a learning-by-example paradigm in which problem solving knowledge is automatically generated according to actual examples presented to them. The knowledge, however, is represented at a subsymbolic level in terms of connections and weights. Neural networks act like black boxes providing little insight into how decisions are made. They have no explicit, declarative knowledge structure that allows the representation and generation of explanation structures. Thus, knowledge captured by neural networks is not transparent to users and cannot be verified by domain experts. To solve this problem, researchers are interested in developing a humanly understandable representation for neural networks. This can be achieved by extracting production rules from trained neural networks. Current rule extraction approaches can successfully deal with problems with discrete-valued inputs but are less efficient when dealing with problems with continuous-valued inputs. This paper presents a novel approach to represent continuous-valued input parameters using linguistic terms (discretization) and then extract fuzzy rules from trained binary single-layer neural networks. An algorithm was developed to extract the most dominant fuzzy rules. The results are very simple rules that can achieve high predictive accuracy. The algorithm was applied to a couple of benchmark pattern recognition problems and a real-world manufacturing problem with promising results.