Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition

Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) for hyperspectral image based face recognition in real time door locking applications

Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods.

Multiobjective Evolution of Biped Robot Gaits Using Advanced Continuous Ant-Colony Optimized Recurrent Neural Networks

This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.

Simplified interval type-2 fuzzy neural networks

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.

Global convergence and limit cycle behavior of weights of perceptron

In this paper, it is found that the weights of a perceptron are bounded for all initial weights if there exists a nonempty set of initial weights that the weights of the perceptron are bounded. Hence, the boundedness condition of the weights of the perceptron is independent of the initial weights. Also, a necessary and sufficient condition for the weights of the perceptron exhibiting a limit cycle behavior is derived. The range of the number of updates for the weights of the perceptron required to reach the limit cycle is estimated. Finally, it is suggested that the perceptron exhibiting the limit cycle behavior can be employed for solving a recognition problem when downsampled sets of bounded training feature vectors are linearly separable. Numerical computer simulation results show that the perceptron exhibiting the limit cycle behavior can achieve a better recognition performance compared to a multilayer perceptron.