Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Inductive functional programming using incremental program transformation
Artificial Intelligence
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Hand Gesture Recognition Using Input-Output Hidden Markov Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Adaptive mixtures of local experts
Neural Computation
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
IEEE Transactions on Evolutionary Computation
The Journal of Machine Learning Research
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
Reduced-Size Kernel Models for Nonlinear Hybrid System Identification
IEEE Transactions on Neural Networks - Part 2
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A hybrid dynamical system is a mathematical model suitable for describing an extensive spectrum of multi-modal, time-series behaviors, ranging from bouncing balls to air traffic controllers. This paper describes multi-modal symbolic regression (MMSR): a learning algorithm to construct non-linear symbolic representations of discrete dynamical systems with continuous mappings from unlabeled, time-series data. MMSR consists of two subalgorithms--clustered symbolic regression, a method to simultaneously identify distinct behaviors while formulating their mathematical expressions, and transition modeling, an algorithm to infer symbolic inequalities that describe binary classification boundaries. These subalgorithms are combined to infer hybrid dynamical systems as a collection of apt, mathematical expressions. MMSR is evaluated on a collection of four synthetic data sets and outperforms other multi-modal machine learning approaches in both accuracy and interpretability, even in the presence of noise. Furthermore, the versatility of MMSR is demonstrated by identifying and inferring classical expressions of transistor modes from recorded measurements.